Explorer Documentation

Gavagai Explorer is a tool to analyze related texts to find common topics, their associated terms, their sentiment scores and their prevalence in the data (number of respondent mentions). Although the primary use case for Explorer is the analysis of open-ended survey responses, it can be used to analyze any set of related texts such as product reviews, Net Promoter Score programs, or social media opinions for a given target-of-interest.

Gavagai Explorer aims to give the analyst a comprehensive and quantified view of the unstructured input data. It identifies the main topics or themes, measuring their importance as a relative strength to the total number of texts. This means that the strength of a topic is comparable across different data sets.

This documentation introduces the main features of the Gavagai Explorer and guides you through using these features effectively.

1 Introduction

All of Gavagai’s services are built on top of a core system called the Gavagai Living Lexicon, which continuously learns languages by ingesting newly published texts from the Internet. Through this process the lexicon learns about the vocabulary of the language and the relationships between words and multiword expressions. From this understanding we can extract useful information in our services. For more information about the Living Lexicon’s website.

In Gavagai Explorer, you can see the effects of the Living Lexicon working in the background by looking for suggestions of related terms in each topic. Each suggestion is taken from the knowledge we have of relationships between words. You can also look for multiword expressions (like ‘San Francisco’) which are built automatically from the system’s general knowledge of language.

2 Getting started with Explorer

Gavagai Explorer is a web-based application that is best experienced using Google Chrome. You can download Chrome here

2.1 Creating an account

You need to create an account with the Gavagai Explorer before you can start to analyze your texts. To get a free trial account, open this link in your browser: https://explorer.gavagai.io, click the ‘Try for free’ button and follow the instructions. On creation, your account will be automatically assigned to a one month free trial with 200 texts on the “Subscription” plan.

2.2 Creating a project

Gavagai Explorer allows you to retrieve insights and statistics from data. The first step in this process is creating a project from your data file or from an online source.

2.2.1 Getting your data ready Preparing an upload file

Both file types that are compatible with the Explorer (.xlsx and .csv) store data in columns and rows. For the Explorer to correctly process your file, the uploaded data should be laid out in a particular way.

Each line of the file should correspond to one verbatim. According to your use-case, this could be a document, a product review or a tweet. All texts for analysis should be located in the same column of your file, as the explore process is carried out on a single column. We will refer to this as the ‘main text column’. In other columns of the sheet, you can optionally include metadata about the text (e.g. date, location, author_id), which can later be used for reference or for filtering your data inside the Explorer.

The file should also begin with a header row, containing appropriate titles for each column.

Here you can find an example file containing hotel reviews that is laid out in the correct way. There are four columns containing metadata, plus a main text column which is entitled ‘review’.

In .xlsx files, it is possible to leverage formulas for processing data, for example: to concatenate text or to calculate time intervals. This is completely compatible with the Explorer for both metadata and textual data. In pre-processing, the system knows to take the resulting value of the formula, rather than the text of the formula itself. Getting online reviews

In some cases you may not have the data necessary for analysis. Explorer provides a way for you to get the data, mostly in the form of reviews, from a number of online sources, without any extra charge.

To get some data start by clicking the “Get Online Reviews” button:

On the resulting ‘Get Online Reviews’ page you need to follow the instructions to enter the URL of a page which has the data you require. We have some more information about retrieving online reviews which includes an informative video.

2.2.2 Analysing your data

If you have your own dataset that you would like to analyse or would like to analyse our sample file, you will need first create a project. Add a new project by clicking on the ‘Create project’ button on the Explorer homepage or under the ‘Projects’ menu at the top of the page. Please note that if you analyse a project from our sample file, you will not be charged any credits for analysis.

If you would like to create a project using a dataset online that you have retrieved, you can create a project by clicking the ID of the reviews dataset and following the instructions on the screen.

Clicking either of these buttons will redirect you to a new page where you can begin the process of creating your project.

The Gavagai Explorer will guide you through the process of creating your project. At any point you can click on the ‘EXIT’ button to return to the homepage. Header Row verification

For your analysis to be accurate, you need to ensure that your dataset has a “header row”, ie, a row of headers for each of the columns in your dataset. The Gavagai Explorer will display the headers that is has detected and if they are accurate, you can move forward to the next step. Naming your Project

At this stage, you can provide a name to your project. If you do not provide a name to your project, the name of the file will be used. Note that you can edit the project name later in the project’s Settings page. Select the Column to analyse

Once your dataset has been uploaded and you have named your project, you can select the column you would like to analyse. Gavagai Explorer will suggest the largest column as the column to analyse, but you can change that at this stage. Additionally, Gavagai Explorer will automatically detect the language of the column for analysis. If you feel that this language detection is incorrect, you can change the language that you use for analysis. Please note that the column which is analysed and the language used for analysis cannot be changed later. Click the ‘EXPLORE’ button when it is enabled, to proceed.

2.2.3 Exploration

Gavagai Explorer will the perform analysis of your data file and once the analysis is complete, you will be presented to view the results in a dashboard or perform modelling) of the topics and groups to ensure more accurate results.

You can view your project in your home screen, along with your other projects. Under the name of each project you can see it’s status, amount of texts and columns, size and the date it was created. It will help you navigate between all your projects. There are also 2 buttons on the right which enable the user to delete a project or to go to its Settings page directly.

You can also arrange your projects in different folders for easier navigation. Add a new folder by clicking on the ‘Create Folder’ button on the Explorer homepage. You can then rename folders, drag-and-drop projects in and out, and delete them if they are no longer needed. Please remember that you can delete folders with projects in them, but the projects will be deleted as well and will not be recoverable.

2.3 Navigate through your projects

Your projects and folders are listed in the Projects page. You can also access each of your explorable project by hovering over the Projects tab. Click on the “Advanced Modelling” or “Dashboard” icon for direct access. The search bar allows you to find a project more quickly by name or id.

3 Overview of the Gavagai Explorer GUI 

When Gavagai Explorer completes analyzing your data at each iteration, it presents the result in a GUI (Graphical User Interface). The GUI is divided into four main sections. At the top of the GUI you can find the Secondary Menu, from where you can navigate to each of these 4 different Project Sections: Advanced Modelling, Dashboard, Settings & Reports and Toolbox.

3.1 Overview Of GUI Functionality

3.1.1 Advanced Modelling Panel

In the top left hand corner of the Explorer GUI is the main control panel for project level settings. Here you will find important buttons that you need to use continually when analyzing data with the Explorer. If you know how to use these buttons correctly, it will help you to analyze your data with Explorer more easily. Update and Save Button

Whenever you make changes in the working panel or configuration panel in the Toolbox page, these changes will not be applied until you press the ‘Update and Save’ button. For ease of access, the ‘Update and Save’ button has been placed to the right of the Secondary Menu, which is always visible while inside a Project. Undo Button

When you make changes in the working panel but before you press the ‘Explore’ button, Explorer will keep track of the current changes and provide you with an ‘Undo’ button that can be used to reverse changes sequentially. Keep in mind that once you have pressed ‘Explore’ and moved on to the next iteration, you cannot automatically reverse the changes using this button. The ‘Undo’ button can be found at the top of the working panel. Sort Alphabetically

The default behaviour of the Graphical User Interface is to sort topics by descending prevalence (i.e. the number of texts in which they feature). By checking the ‘sort alphabetically’ box you can choose to instead sort your topics by alphabetical order. Topics inside a group will also be sorted alphabetically within that group. Group Pinned Topics

If this box is checked, topics that you have pinned will be shown at together at the beginning of the topic list. Unpinned topics will follow. (For more information on pinning and unpinning, see the section below on topic level buttons). Show Only Pinned Topics

Checking this box will display only the pinned topics in your project. Unpinned topics will be hidden. Show Only New Suggestions

For every topic in your project, the Explorer suggests additional terms from your data which could also be relevant to your topic. As you progress with your exploring, it is likely you will add extra terms to your topics as you see fit. As the suggestions for a topic are based on the terms it already contains, new suggestions will occur each time you save and update your project (assuming new items have been added). Checking ‘only new suggestions’ will make the interface display only the suggestions that are based on the most recently added terms, hiding those that have already been seen. Coherence For Suggestions

As previously mentioned, the suggestions for any particular topic are based on words that are semantically similar to the existing topic terms. As a result, as the number of terms in your topic gets larger, the number of suggestions increases. For larger topics, the list of suggestions can become very long. To make such a list easier to read, you can check the “coherence for suggestions” box. This means that the system will only display words that are semantically similar to a significant number of the existing topic terms, providing a more coherent list. Otherwise, the default setting shows words that are semantically similar to any single term in the topic. Topic Size Distribution

At the bottom of the panel, you can find a small but useful info-graphic. Here you can see a rough overview of the current topics in your data and the distribution of their sizes, without needing to scroll down the list. You can easily see, for example, if you have many similarly sized topics, if you have just a few large topics and many very small topics, or other shapes of distribution.

3.1.2 Topic Level Buttons

Below the main project panel you can find the list of current topics. Each topic has its own small panel with buttons related specifically to that topic. Pin Button

Each topic has its own pin button. When you pin a topic by pressing this button, it tells the Explorer that you want to always keep track of this topic in your list, regardless of its frequency. The Explorer usually displays the N most frequent topics, where N is customizable by the user (3.3.2: Project Settings). The pinning functionality is particularly relevant when you filter texts, remove terms or add data. It can also be useful to keep track of a topic which is very small but very important. Other significant applications of pinning are when you create models from your existing projects (see chapter 8) or when you export the result of your analysis in a full MS Excel format (9.1 Save as Excel and Full CSV). Explorer will also provide Theme Wheels for your 6 most frequent pinned topics (6 Explorer Theme Wheels). Topic Scroll Button

For each topic we have a topic scroll button in the working panel (see the previous picture). When you press this button the Details Panel will automatically scroll to the selected topic.

3.2 Overview of the Dashboard: Graphical Results of Topic Analysis

You can click the “Dashboard” button in the Secondary Menu to navigate to the Dashboard view of your project. Note if you want a walkthrough example with the calculations involved you can visit the support page or click here.

3.2.1 Main Dashboard View

After entering the Dashboard view you will see different graphs that represent the results of your project. When you created your project with a column of text to analyze with The Gavagai Explorer, you might have had a numerical column with a Grade or some kind of rating score with each text. On default all the graphs will use sentimental scores with the analysis, but if there is a numerical score in the data set then the graphs will use that. That is, the average grade/nps will be taken from reviews mentioning a certain Topic and taking that sub-group of reviews and averaging the grade of those reviews. Throughout the explanation Sentiment, Grade, NPS, or rating could be used interchangeably.

In the top right you can share your dashboard as a website. By pressing the button you will generate a new website and you will receive a link to be able to share with anyone. Note by visiting the link, it will not allow you to edit any of the graphs, only see the graphs. Occurrence Grade Matrix or Occurrence Sentiment Matrix or X-Y Scatter Plot of Topics

In this graph the different topics found from The Gavagai Explorer are plotted along two dimensions, the x-axis or left to right is how often the topic is mentioned and the y-axis or up to down is that specific topic’s sentimental score or average topic’s grade score. In other words Topics in the top right quadrant are topics that are both spoken about a lot and have a high sentiment or average grade. Note in this graph the header says Occurence-Grade Matrix because there was a numerical score column called Grade in the dataset that was uploaded. If there was no such score, then it would have defaulted to Occurence-Sentiment Matrix. You can click any of the topics to go to the Topic View. Read more about the Topic View in 3.2.2 Topic Dashboard View

Not all topics are included in the matrix - the topics that the system detects as “most important” are included by default. All topics that are pinned (or belong to groups that are pinned) are always included in the matrix. Important Topics

In this graph the different topics found in the Occurrence Grade Matrix that had the highest or lowest Sentiment or Grade from the average are plotted here. The height of the bar is the difference from the average in percentage. This graph is helpful because you can see which topics are pushing the average Sentiment or Grade higher or lower than average. You can click any of the topics to scroll down to the Related Topics graph. In this case, the documents that match the topic “wine” have 10% higher average grade than the average grade of the documents matching all other topics in your dataset.

Related topics are topics that are frequently mentioned along with the respective important topic (see here for more information). In this graph, for each of the important topics, we show you what the system identifies as the most important related topics - These are the related topics, which when they occur in a document along with the important topic, have a higher or lower average grade than that of all the documents matching the important topics; Looking at an important Topic we can see important related topics that help drive that important Topic’s average Sentiment/Grade up or down. For important topics with an average grade or sentiment below the average grade or sentiment for the entire dataset, we show the negatively driving related topics, and for the important topics with a higher average grade, we show the positively driving related topics. The height of the bar is an indication of how much the related topic causes the grade or sentiment score to vary from the average grade or sentiment score of the topic.

For example, in this case, documents matching the topic “wine” and also match the topic “complimentary” have an average grade which is ~15% higher than that of the documents matching the topic “wine”. Conversely, documents matching the topic “morning” and related topic “net” have an average grade which is 30% lower than the average grade of the documents matching the topics “morning”. All Topics Bar Chart

In this bar chart, each bar represents a topic and the height of the bar represents a value depending on the toggle activated. In the first graph, the white bars represent the topic’s occurrence and in the second graph, the blue bars represent the topic’s average sentiment or grade. Comparison Graph

If you click Add New Graph, there should be a menu that appears to choose between two graphs. The two graphs allow you to create analysis into your data using your other metadata columns (the column that is not the text review column that was chosen to be analyzed) such as the Grade, or a demographics column such as city or age.

Let us begin with speaking about Grouped Comparison graphs. Grouped Comparison Graphs

There are four fields that need to be configured. At the top you can click the little edit button to edit the name of your graph.

  1. Choosing a Topic, this is which Topic will be analyzed
  2. Choosing a Metric, there are several options, the Topic’s Sentiment, Occurrence, or NPS/Grade. This will be the Y axis values in your grouped comparison graph.
  3. Depending on which Metric is chosen:
    • Sentiment: You can choose Positivity, which is the Topic’s Positivity score, Negativity, which is the Topic’s Negativity score, or Net Sentiment which is the average Positivity Score minus the Negativity Score for all documents matching the topic.
    • Occurrence: You can choose Absolute, which is the absolute number of mentions speaking about that topic. Absolute Percentage is the percentage of mentions for that specific topic across the metadata columns (number of times a specific topic is mentioned / total mentions for that topic). Relative Percentage is the percentage of mentions for that specific topic for that specific metadata value (number of times a specific topic is mentioned / total number of reviews that match that metadata value)
    • NPS / Grade: This is the Topic’s average nps, grade, or numerical score’s average that the reviews that spoke about the topic had.
  4. Data Layer Column: This is the column that values will be chosen from that your Topic will be split by. Note you can filter out values from the chosen data layer column. You can have up to two data layer columns.

After configuration of the Grouped Comparison Graph and clicking Create Graph this is what it should look like. Where the Topic Room’s absolute occurrence across the different Grade score values, 1, 2, 3, 4, and 5.

Below are examples for the same Topic but with the three different Occurrence options.


Relative Percentage:

Absolute Percentage: Time Series Comparison Graphs

If you have a time series column in the data you uploaded then you can also compare Topics over time.

There are three fields that need to be configured. At the top you can click the little edit button to edit the name of your graph.

  1. Metric that will be compared.
    • Sentiment
    • Occurrence
    • NPS / Grade
  2. Depending on which Metric is chosen:
    • Sentiment: You can choose Positivity, which is the Topic’s Positivity score, Negativity, which is the Topic’s Negativity score, or Net Sentiment which is the average Positivity Score minus the Negativity Score for all documents matching the topic.
    • Occurrence: You can choose Absolute, which is the absolute number of mentions speaking about that topic. Absolute Percentage is the percentage of mentions for that specific topic across the metadata columns (number of times a specific topic is mentioned / total mentions for that topic). Relative Percentage is the percentage of mentions for that specific topic for that specific metadata value (number of times a specific topic is mentioned / total number of reviews that match that metadata value)
    • NPS / Grade: This is the Topic’s average nps, grade, or numerical score’s average that the reviews that spoke about the topic had
  3. Add a New Curve. You can click the edit button to change the name of this curve. This will allow you to select a Topic which will be rendered across your time series from your column with the time data.
    • You can choose a filter from your other metadata columns such as Grade to only see the Topic over time but will be filtered using the other metadata columns. And from that filter, you can choose different values to be used as a filter.
    • You can add up to five curves per each time series graph.

After configuration of the Time Series Comparison Graph and clicking Create graph this is what it should look like. Where the Topic Staff’s absolute occurrence across time is plotted. Note there are two curves which represent the Males and Females speaking about the staff. Male or Female were from the gender column from the data set, depending on your own data set you might have other demographics data that you can use.

3.2.2 Topic Dashboard View

After clicking a topic on the Occurrence Sentiment/Grade Matrix or Related Topic or the Topic and Sentiment/Grade graphs you will be taken to the topic view of the Dashboard.

  1. This is a quick summary of the Topic. Here if you have a Grade or Numerical score column, it will show the average score for all the reviews mentioning this topic otherwise if there is no score column it will show the Positive/Negative/Neutral sentiment breakdown for this topic. Note the Grade score’s coloured green because the topic’s average grade is higher than the total project’s average grade. This is indicating the topic is correlated with a higher review score. It also shows the topic’s occurrence in percentage of the entire project.
  2. If there was a time series column then there will be a combo bar and line chart. The bars represent the topic over time and the height is the topic’s occurrence in percentage on that time series point in time. The line represents the topic’s net sentiment in percentage on that time series point in time.

The graph titled Topics Related to Staff is a barchart that represents other topics mentioned and their occurence when the specific topic is mentioned, in this case Topic: Staff. The height of the bar chart represents the percentage occurrence of a Related Topic when, in this case Staff was mentioned.

The respective topic is also highlighted on the Occurrence Matrix graph which was explained above.

The graph titled Related Topics Over Time is a line chart over time where each line represents a Related Topic’s occurrence in percentage of how often the Topic View’s Topic was mentioned. That is, from all the mentions of the Topic Staff in this example, how often was the Related Topic: Friendly mentioned in percentage over time. 100% would mean every mention of Topic: Staff, the related Topic: Friendly was also mentioned. You can toggle the lines by selecting them below the chart.

Example Texts are example texts of the Topic where you can read and click load more to read more examples.

3.2.3 Editing capabilities in the Dashboard

We offer editing capabilities for the graphs in the dashboards. To edit a graph, click the ‘Gear’ icon that is visible when you hover a graph. Renaming the Dashboard Graphs

While editing the dashboard you have the possibility to rename the graphs to better reflect what you would like them to convey, by clicking the edit icon. Resizing the Dashboard Graphs

You can also re-size graphs by toggling between half and full width. Drag the re-size icon of the graph to the left or to the right to either increase or descrease the width of the graph.

3.3 Settings and Reports Page

The Project Settings and Reports Page is where you can see and edit information about a project:

3.3.1 Change Project Name

You can change your project’s name in the Settings Page. There exists a pencil icon, at the top of the page, next to the title of the project, which allows you to do this.

When you click on it, the title of the project is editable and the title is saved once you click away from the text box or press the Enter key.

3.3.2 Project Settings

You can also update your project settings in the Settings and Reports page. In Explorer you can configure different settings for your data analysis. These can be applied to the account as a whole or to individual projects.

Once you have updated the relevant settings, you can click the ‘Update and Save’ button to save the settings and automatically re-explore the project with the saved settings.

Here each of the settings is explained briefly:

However, if you wish to apply settings to projects across the whole account, you can go to the Account page from the main Explorer screen. Each time that you make changes to your account, they will be applied to any new project which is created. For pre-existing projects, account settings will be applied the next time you explore. However, if there are project level settings already in place, these will override the general account settings.

3.3.3 Appending and Deleting Additional Data Adding Data to your Project

It’s also possible to add several files to an existing project by uploading another CSV (.csv) or Excel file (.xlsx, .xls). The file needs to have the same columns as the one you initially uploaded and should NOT contain a header Row (that first row which names the column). You can also see your upload history on this page. Drag and drop the new file or choose one from your computer.

The project’s status will change to Appending and when it switches back to Ready you can start/continue working on analysis. You must re-explore the project to have the new data taken into account. The amount of rows in the project should change according to the appended file, but if there are pinned topics and/or groups in the project - they should all remain in place. See section Pin Button You can see the history of uploads to every project: date added, file name, format and the amount of rows. Deleting data from a project

As mentioned in the section above, it’s possible to add more data to an existing project. In the same way, you can delete data that was appended. Scroll down to history of all appended files. Choose the one you would like to delete and click on the trash bin symbol.

The confirmation pop-up will appear and after clicking Yes, delete the project status will change to Deleting.

During the Deleting status the append history will be grayed and it’s not possible to make any more changes until the deleting is complete. It can take some time depending on the file size and you can reload the page to see if the status has changed. After deleting the amount of rows in the project should change accordingly.

3.3.4 The List Of Reports

In this list you will see entries for all reports created in the project. If a report is in progress it will be printed in black, and once it is finished it will become red and clickable; to download a finished report you click the report name. You can also delete a report by clicking the trash can icon at the right of the report entry (you will be presented with a confirmation pop-up to ensure you don’t delete a report accidentally).

3.4 Toolbox Page

Once you have explored you project for the first time you through the project creation flow, you can also (optionally) apply the following optional configurations to your project in the “Toolbox” page of the project, where you can apply a template model to your project, set up filtering conditions and remove terms from your analysis, etc. Once you have applied your filters and customizations, click the ‘Update and Save’ button for these customizations to take effect.

3.4.1 Applying a Template Model (Optional)

You can apply a model as a template to your project. In chapter 8 we explain how models are created and used in Gavagai Explorer.  

3.4.2 Filter Conditions by MetaData (Optional)

You may optionally set up some filtering conditions on values from other columns than your main text column. In the drop-down menu you will find those columns that are candidates for filtering - this means those columns that have a few different values repeated over and over, like ratings, or gender. Then you can choose which values are accepted for that column. After choosing the accepted values, save your settings by clicking on the ‘Add filter’ button.

You might apply filtering conditions to divide your data into smaller groups and analyze each group in Explorer independently; e.g. for employee’ reviews of a large company you might divide your data to company’s departments, or you might divide it into two group of males and females. You might also filter texts based on the level of respondents’ satisfaction by selecting specific values of ranks or ratings.

Another application of filtering columns is where you have very large amount of data which cannot be analyzed by Explorer at once. In this case, you may, for instance, filter data based on time periods (e.g. monthly basis).

3.4.3 Filter Conditions by Date Range (Optional)

It’s also possible to specify the date format and select a particular date range for filtering. In the Date range filter drop-down menu choose the column that contains dates and you will see samples, it should help you choose the right column. You can also change the date format in the field below and test it - the dates should match the samples.

Then you can choose the date range of the texts you would like to filter out. Just set from/to dates and click on Add filter.

When the filters are applied, Explorer button will show that you are working with the filtered results. To go back to the complete dataset, you need to remove the filters and click on Update and Save again.

When you are done configuring your project, you can click on Update and Save to analyze your project.

3.4.4 Concept Filtering

You can filter the contents of the project based on concepts that you have in your account. For more information about creating and using concepts see section 7. Concepts can be used to qualify the texts that are part of your current analysis - texts will be included in the analysis if they match the configuration of the concept filter. This can be done when producing a report (as described in section 9.1.3), and it can be done when exploring a project interactively.

A concept filter works by looking for its terms in the texts of the project, and depending on the settings described below, matching texts will be included or excluded in the analysis. Texts are matched on any terms in the concept and for a match to be valid it must be exact.

The concept filter configuration can be done at any time while working with a project and you can find the control for the configuration in the main configuration screen for the project.

In the drop-down menu you will find all concepts available in your account. If you select a concept from the menu a filter entry will appear below the menu:

The concept name is followed by its ID and two options:

To apply the concept filter you need to update and save the project by clicking the ‘Update and save’ button. If you remove the concept filter by clicking the small x at the right all texts will be included as usual in the next update of the project.

When the concept filters are applied, Explorer button will show that you are working with the filtered results. To go back to the complete dataset, you need to remove the concept filters and click on Update and Save again.

3.4.5 Include MetaData (Optional)

If metadata exists in a given project, you can choose to add up to 3 metadata columns from a drop down menu. The information from the columns you specify will be added to the text examples of the topics - for better understanding and analysis. You can change the columns and see the result directly - no re-exploration is needed.

3.4.6 Generate Stories (Optional)

Stories give you another way to look at your analyzed data and let you organize your data independently of the exploration results. It shows the main topics using the clustering algorithm which has been optimized for analyzing editorial and media content like newspaper articles. It will find the common events and topics across all texts and also present the stories graphically in an easy-to-understand manner. Please note if you want to generate a report (such as the Excel report) there is a maximum of 2000 reviews for your project. To turn on Stories your project must contain two meta-data columns:

You also need to pick a topic for which to create the stories. Choose the columns from the drop down menu, click Generate stories button and your stories will be generated on-the-fly.

3.4.7 Questions Filtering (Optional)

You may optionally set up a filtering condition on your project texts. To do so, select “Only questions” and explore the project again. The analysis will only include the questions identified in the texts.

3.4.8 Languages Filtering (Optional)

You may optionally set up a filtering condition on a language available in this project. Select a language in the drop-down menu containing all the identified languages and explore the project again. Only the texts in the selected language will be analysed. When you switch languages, the project’s current model will be cleared but we will present the option to save the project’s pinned groups and ignored terms as a template model. You can choose to continue without saving the model if you prefer.

4 Topics in Gavagai Explorer

Topics are the core of the text analysis in Gavagai Explorer. They are basically the main subjects in your data. For instance, in hotel reviews, topics like hotel, staff, room, restaurant, etc. are usually among main topics. In addition, there are usually another type of topics in the texts which are not related to a hotel in general but become a topic for one specific hotel because of the frequent mentioning by respondents. For example, if you have a data including hotel reviews and many of the respondents refer to many items in the hotel as great items (e.g. the hotel, the restaurant, their room, etc.) then “great” becomes a topic in your project. On the other hand, if many of the respondents complain about a specific issue in the hotel; e.g. renovations, then that issue also becomes a topic in your project.

Each topic in Explorer has a name and it includes one or more terms. The topic name is a label assigned by the system or the user for referring to the topic, while the terms are the actual words (or sequences of words) from the data that the topic is composed of. Each term can be included in only one topic. We say a document includes a topic if it includes at least one of the terms in that topic. Then, the frequency of a topic is defined as the number of documents that include that topic, divided by the whole number of documents.

Explorer finds the main topics in your data and it makes it possible for you to revise them. When you run explorer on a dataset for the first time, Explorer shows you the 30 most frequent topics throughout all the texts.

For each topic, Explorer considers a specific area for that topic in both working and Detail panel. We refer to the working and detail panels as simply the topic area. In addition, there is a blue box for each topic in the working panel which includes the name of the topic. We refer to this box as topic box. At the right side of each topic box, you can see the topic expander which contains two numbers. Here you can see the topic box and its expander for the topic “hotel” for data including hotel reviews.

The first number (in green) is the number of including terms in that topic and the second one (blue) is the number of suggestions (see next section). When you click on topic expander you can see the including terms and suggestions.

In the following, we explain the main elements in each topic and we guide you through revising them.

4.1 Terms in Explorer

4.1.1 An Introduction to Explorer language Capabilities Paradigmatic Neighbors

Explorer searches for terms in your data with more complexity than a simple search engine. Let us explain by an example. Consider the word “income” in the following sentences:

Many people are dissatisfied with their income.

I can’t get by on such a small income.

The company’s gross income grew considerably this year.

The single most important measure of a company’s profitability is net income.

Compensation is far below the market.

In the first two sentences, income has been referred to as a general term while in the two latter sentences the terms gross income and net income are specific types of income. In the last sentence, the word income is not present, but we have the word compensation which is a synonym of it. Words like income, net income, gross income and compensation are called paradigmatic neighbours. Paradigmatic neighbours are semantically related words which are used in text or speech for related objectives. Paradigmatic neighbours can be synonyms, like “income” and “compensation” in above examples. They can also be words that do not have same meanings but they are related in some way; e.g. “fork” and “spoon” which are both used for eating. N-grams

An n-gram is a sequence of words which frequently appears in the same order in text or speech. Generally, it is important not to split n-grams as it might result in information loss. For instance, “water supply” is an n-gram consisting of two words “water” and “supply” while none of these words can define it individually. The word “San Francisco” is an n-gram which is different from both “San” and “Francisco”. Another capability of Explorer is to identify n-grams in your data and not split them. From now on, when we mention terms in this document we mean n-grams. The value of variable n is dependent on the corresponding language and it is usually between 1 and 3 or more.

4.1.2 Topic Terms and Synonyms

As mentioned, each topic can include one or more terms. Terms included in each topic are words that each can define that topic independently. Each term can belong to only one topic. For each topic, Explorer shows examples of texts including the terms in that topic. You can find them in explorer Detail panel. Only the 10 most frequent terms will be shown when the exploration results load. If there are more than 10 terms, you can see all of them by clicking the ellipsis button. You can also filter the examples by clicking on specific term(s).

In the Detail panel, each term can be either selected (shown in orange), or non-selected (shown in green). When you explore a project, all terms in all topics become selected by default. You can deselect a term by clicking on it in the Detail panel. The examples and sentiments which are shown for a topic in the Detail panel are derived from the selected terms for that topic. Therefore, when you select or deselect a term, you need to press show sentiments and show examples buttons to see the updated results.

When the project is explored for the first time, the Gavagai Explorer uses its language resources and algorithms to automatically merge words that we consider as “synonyms” into the same topic and these synonyms are taken into consideration when performing the analysis. There are multiple reasons that terms can be considered “synonyms” and merged into the same topic Static Synonyms

The Gavagai Explorer’s living lexicon constantly looks for new words the it considers synonyms. Once it has identified words that are strongly related, a Gavagai administrator can mark these words as Synonyms so that they are included as part of the same topic during exploration for all users. Collaborative Synonyms

The Gavagai Explorer also utilizes users’ anonymized topic models to automatically automatically merge terms into the same topic. If a certain number of users individually and without knowledge of each other accept synonym suggestions that makes it more likely that those terms will automatically be included as part of the same topic for other users in the future. This never happens with a single accepted suggestion or even with several by a single account. Morphological Synonyms

In most languages, there are several words that have the same root but have slight variation due to grammatical rules (such as ‘teachers’ and ‘teacher’). This is especially true in certain languages such as Finnish and Croatian, in which there can be over 100 variants of the same word. In certain languages, the Gavagai Explorer also merges the morphological variants of words together into the same topic to ensure the exploration results are more accurate.

4.1.3 Topics with Sentiment words

Sentiment words are words that have some inherent sentiment or emotion; for example, “good” or “horrible” are sentiment words since they are positive and negative words respectively.

While there are benefits to having “sentiment topics” (topics containting sentiment words), any sentiment results computed for these topics will be adversely skewed since the sentiment calculation algorithm does not include the topic terms themselves while checking for the sentiment words about the topic terms.

As a consequence, sentences such as “the house was nice”, will receieve a positivity score of 0 when sentiment analysis is performed for a topic containing the word “nice”.

Since sentiment analysis plays an important part in the insights retrieved from Gavagai Explorer, sentiment words will never be automatically included in any topics created by the system. That being said, the Gavagai Explorer will always allow you to manually create topics which contain sentiment words, but the sentiment results of these topics maybe skewed due the reasons listed above.

4.1.4 Topic Suggestions

For the terms included in a topic, Explorer automatically finds their paradigmatic neighbours and shows them to you as Suggestions. You can see more suggestions by clicking on Get words button. When you hover over a suggestion in the working panel, a question mark will appear. You can see the examples of the texts including that suggestion by clicking on this question mark. When you add new terms to your topic, Explorer updates the suggestions list by adding the new possible suggestions. The number of new suggestions is denoted by a little red square under the total number of suggestions. In addition, the new suggestions are shown with a slightly different color from the old ones with a small red square at the top right side of them.

Terms and suggestions are listed in descending order according to their number of mentions. This number is indicated at the bottom right when hovering over a word.

4.1.5 Adding and Removing Terms Adding Terms

You can always expand your topics by adding new terms to them. To add terms, you need to open terms and suggestions by clicking on the topic expander. Then you can either click on suitable suggestions or write them manually in the terms entry field. An auto-complete drop down menu presents terms from topics and related topics as candidates, and you can either select several options or add all of them at once.

To see more suggestions, you can click on Get words button. 

Note that a term including in one topic might appear in the suggestions of another topic. For instance, consider the the topic income including the terms “income”, “salary” and “salaries” and the topic compensation including the terms “compensation” and “compensations”. Since the two words compensation and income can be used interchangeably in the same contexts, you see the term “compensation” as a suggestion for the topic income.

In this case, if you choose to accept compensation for the topic income, Explorer will merge the two topics automatically (read more about merging in 5.2.3 Merging Topics). This is basically because the terms in each topic are assumed to be synonyms (so if two terms in two different groups are synonyms then all terms in that groups are synonyms). In figure below you can see the new topic income after automatic merging. The topic compensation will become disabled. The next time that you press Explore, you will see the new statistics for the new topic income and the topic compensation will be removed from the list.


The full search feature of the auto-complete drop-down menu presents terms from the project that are not yet members of any topic. As you type your auto-complete term, the full search finds all terms and multi-word expressions that match. Beware, however, that the search only finds multi-word expressions that the Explorer system knows about. This means that you cannot search for expressions that are two words or more unless these have been recognized as multi-word expressions in the system. Here is an example: the multi-word expression “san francisco” is a valid search term since it is so prevalent in ordinary language that our system knows about it as such. On the other hand “daniel san” is perhaps not as common and therefore a search for those two words will not get a hit even if the project in fact contains one or more texts with those words in sequence. Why not allow the search to find any arbitrary sequence of words you might ask. Similarly to much of the Explorer’s functionality in general, the full search feature is focused on finding the most important expressions as opposed to everything in detail. This design is a careful balance of utility and performance considerations.

Results from the full search are shown in the auto-complete drop-down menu under the heading “Full search”. Removing Terms

You can remove terms from topics by clicking on them in the working panel. When you remove a term, you might still see it in the list of suggestions of other topics and therefore you can add it to them. Note that when you remove a term from a topic, Explorer automatically pins that topic so that you would not lose the topic in the list of topics in case the topic becomes infrequent.

You can also ignore a term from all of your topics by writing it in the text box under Ignore Terms at the top of the details panel and pressing add term (previous figure). When you remove a term, Explorer ignore it in your analysis, however, note that texts including that term are still available for contributing to the topics.

For each topic, Explorer finds the words that are tightly connected to the terms in that topic, and shows them to you as “related topics”. By tightly connected we mean words that appear together repeatedly and closely in the same sentences in different texts. For each topic, the list of related topics can be found under the topic box. You can also find the frequency of each related topic with respect to that topic in front of it. Related topics are ordered in the related topics list based on their frequency and also their closeness to the topics.

Related topics can give you a better understanding of the topics. For example, consider the topic “the restaurant” which is a frequent term in a hotel reviews data set. As you see, the terms “closed”, “renovation” and “under construction” are tightly connected to this topic which means most respondents are complaining about renovations when they mention restaurant in their responses.

You might have noticed that the term “breakfast” is at the bottom of the list although it is more frequent in the topic comparing to the upper terms. This is due to the fact that  both frequency and closeness are taken into account in the process of listing the related topics.

For any subset of the set of terms and related topics, you can filter the texts examples that contain all the words in that subset. You only need to click on them in the detail panel to filter texts.↓

4.3 Sentiment Analysis

Gavagai Explorer applies word based or lexical based sentiment analysis principles to quantify the sentiments behind expressed opinions. In the Details Panel, you can see the quantity of three basic sentiments for each topic or group; that are Negativity, Positivity and Skepticism (next figure). When you filter texts by selecting terms and related topics in the details panel, the sentiment values will be updated as well. When you export the result of your analysis into excel, you can see 8 sentiment values for each single text (see 9.1.2 Sentiments). You can also select one or multiple topics for sentiment analysis when you export. This will give the sentiment scores for your selected topics in the report (see 9.1.6 Sentiments Per Topic). Moreover, you can model your own sentiments by using Explorer Concept Modeler and then Explorer will analyze your data for these Concepts (read more in 7 Explorer Concept Modeler).

4.3.1 More about Sentiment Analysis in Explorer

Explorer performs two different types of sentiment analysis; new and classic sentiment analysis. Explorer selects the new sentiment algorithm by default. You can switch to the classic sentiment algorithm in your advanced configuration settings located on the Account page. The Explorer will strictly apply only one algorithm for the entire Explorer project. When you set an algorithm for your account, you must click Update and Save to apply the new algorithm.

The classic sentiment algorithm performs a sentence level sentiment analysis for each topic. The new algorithm performs a topic level sentiment analysis for the topics. More on both of these algorithms in 4.3.4 The Sentiment Analysis Algorithms that Determine the Score.

This difference is important when a sentence has different topics with different sentiments. If a sentence is so short and has only one topic: “The room is good”, then using either the new or the classic algorithm results in the same score.

4.3.2 The Sentiment Scoring System

The system’s sentiment scoring gives a score for each Sentiment word used to describe a topic. Note, amplification words such as “very” increase the score. And negations such as “not” impact the score by reducing the score.

For example:

The room was good. Has a 1 for Sent: Positivity.

The room was really good. Has a 2 for Sent: Positivity.

The room was not good. Has a 1 for Sent: Negativity.

There are eight sentiment categories: Skepticism, Fear, Violence, Hate, Negativity, Love, Positivity, and Desire. The categories avoid ambiguity of sentiment scoring by not containing words that can be inside many categories. For example, “cheap” could have different sentiments. For example, “X is cheap”. This sentence is ambiguous. If X is beer, it is positive, if X is a wedding ring it is negative. Thus, if there is a word that you believe should be in the sentimental category then please try to use the word in sentences that are both that sentiment and the opposite sentiment to check. In contrast, words such as “good” that express only one sentiment are added to their respective sentiment category.

When a word from the eight categories like, Positivity, for example “good” is used to describe a topic such as hotel. The sentiment score will give a simple 1 for the topic hotel. The sentence, “The hotel is good.” would receive a 1 for positivity, and 0 for the seven other sentiment categories. If you would like to see the actual numbers for the sentiment scores for each topic, you need to export the analysis as an Excel or CSV file and select the topic you would like to see. See 9.1.6 Sentiments Per Topic for more. The sentiment score given for each topic varies when combination of words happen such as “really good”.

4.3.3 The Sentiment Graphs in the Web Application

In the web application the sentiments are in the right hand side with a percentage breakdown and also in absolute values. We see the sentiments Negativity, Skepticism, and Positivity in a bar. This sentiment percentage bar is calculated by using the topic that the percentage bar is referring to. All the topic’s Positivity, Negativity, and Skepticism scores are summed. For each sentiment category this sum can be found next to its name above the sentiment bar. Then, each of Positivity, Negativity, and Skepticism scores are divided by the sum to return a percentage. To see the text examples which are relevant to one specific sentiment category, you can click either on its name or on its color in the bar. It’s also possible to change basic sentiments in the bar to any other sentiment Gavagai Explorer supports: Love, Hate, Violence, Desire, Fear. You can even have Neutral sentiment calculated and shown in the bar - this can be adjusted in Project Settings or Account Settings. See 3.3.2 Project Settings.

The sentiments are also detailed in the Dashboard, represented in a circular chart.

4.3.4 The Sentiment Analysis Algorithms that Determine the Score

Consider the following text uploaded to The Explorer:

The text include two different topics and for each topic there is a different opinion. The new sentiment algorithm has more precision in getting the expressed opinions for individual topics and calculating the sentiment scores. The room topic has a 1 for positivity. And the staff topic has a 1 for negativity. Looking at the web application there is 100% positivity for room and 100% negativity for staff.

Moreover, for the new sentiment algorithm, not all topics are relevant for sentiment analysis. Explorer only performs sentiment analysis for topics (or topic terms) that are not sentiment terms themselves.

Consider the sentence “The room is nice”. If we ask what is the expressed opinion about room in this sentence, one can say it is positive, it is nice. Now suppose that instead of room we focus on nice as a topic. Does it make sense to ask “what is the expressed opinion about nice in this sentence”? The answer is no. In fact nice is not a focus topic in this sentence. The sentence is explaining room and not nice. Nice is the word by which the sentence expresses opinion about room. This is the reason behind why we do not perform sentiment analysis for sentiment terms.

The classic algorithm takes into account sentimental words of an entire sentence. Regardless of what topics the sentimental words are describing. For example, the sentence, “The hotel was good and the staff are good.” will return a 2 for positivity for the topic hotel and the topic staff. Thus, there is less precision in analyzing the sentiment. In addition, for the web application, the detail panel will only display sentiment when 10 texts have sentiment for a topic.

The sentence uploaded as an excel file to The Explorer (repeated 10 times since we need at least 10 texts):

returns for the topic room, 1 for Positivity and 1 for Negativity. And in the web application, we see the topic room have 50% Negativity and 50% Positivity. This is a coarse approach and can be inaccurate in calculating sentiments for topics.

However, there are some benefits. The classic algorithm is useful for calculating what the sentiment is on a sentence level is. Which is important for comparing individual documents. In addition, there is a slight speed advantage.

4.3.5 Sentiment Analysis and the Text Examples in the Web Application

In the web application and in the detail panel, Explorer shows examples of texts which are included in the topics. These texts are examples featuring the terms of the topics selected in the detail panel (shown in orange). At the top of each example area, Explorer shows sentence(s) that feature the topic terms with bullet points. To the right hand side of each sentence, their topic related sentiments are shown by colored circles, where each color shows the same sentiment as in the sentiment bar. You can see the complete texts by clicking on ‘Show original text’ at the bottom right corner of the examples. Here you can also see the sentiments found in the entire text (not specific to the particular topics). Note that Explorer only shows those sentiments that you have chosen in your project.

You can view the text examples corresponding to each sentiment by clicking the sentiment in the sentiment bar or in the sentiment legend; these examples are filtered by the selected sentiment. If you would like to see all examples matching the topic terms, unfiltered by sentiment, you can click on the “Back to all examples” button at the top of the text examples list.

4.3.6 Overall Sentiment of the Project

At the top of the detail panel and under Project Summary, we have the overall sentiment of the project shown in a colorful bar. This feature is dependant on your active sentiments, your sentiment settings and the neutral sentiment if it is on or off. Based on these settings and the contribution of each verbatim to each sentiment, the percentage of each sentiment is calculated for all verbatims. Each time that you load/re-explore a project, you need to click on show sentiment button to see the sentiment bar. Here you can also see text examples by clicking on the show examples button, and you can filter the examples for a specific sentiment by clicking on the related color on the sentiment bar.

4.4 More about n-grams in Explorer

Now that you have learned about topics, topic terms and sentiment analysis, it is worth learning a bit more about n-grams in Explorer as well. As mentioned before, n-grams are topic terms including multiple words, for example “San Francisco”.  Explorer identifies n-grams in your data and show them to you as topics if they are enough frequent. You can also add n-grams to your topics manually. The most important characteristics of the n-grams is that they are treated as one single entity, and therefore, the uni-grams included in an n-gram cannot contribute to topics individually. This is the case for both topic counts and sentiment analysis. As an example, suppose that you have an n-gram “junk food”. Also assume that you have a topic FOOD which includes “food” as a topic term but not “junk food” as a topic term. Now for the sentence “I don’t like junk food at all”, for Explorer the topic FOOD is not included in this sentence because of “junk food” being a bi-gram. 

4.5 Auto-Add Terms in Explorer

When working with topics you may notice that there could be several n-grams containing a general topic term you are interested in (for example, you could be interested in the general topic term ‘food’ and the Gavagai Explorer detects n-grams ‘fast food’, ‘junk food’ and ‘food delivery’) and you may want to include all variants of the general topic term in the topic. One way of achieving this is to utilize the auto-complete drop down menu and selecting all variants of the term. The drawback of doing this is that once data is appended to the project and new n-grams are detected in the data, the same process must be repeated for all such general terms if you wish to keep the topic definition up-to-date with the current data.

Alternatively, you can utilize the ‘Auto-Add Terms’ feature:

When you add a term in the ‘Auto-Add Terms’ section of the topic, Explorer will automatically add any n-grams containing this term to the topic when the project is explored. Any n-gram which matches an Auto-Add Term but which is already part of another topic will, however, not be included in the topic. Once a project is explored after data is appended to it, newly detected n-grams in the new dataset matching any Auto-Add Terms will be added to the appropriate topics and an email notification will be sent specifying the terms which were added to the topics.

If there are terms which have automatically been added to a topic as part of an Auto-Add Term, and you would like to exclude one or more of these terms from the topic (for example if you have an Auto-Add Term ‘food’ and you want to exclude the automatically added n-gram ‘cat food’ from the topic), you can always click the terms, as usual, to remove them. However, this term will now be added to the project’s Ignore Terms to ensure that the term is not automatically re-added to the topic. If you want this term added another topic instead, remove the term from the project’s Ignore Terms and then add the term to the other topic.

5 Revising Topics

In this section, it is explained how to revise your topics by Explorer according to your assumptions and needs.

5.1 Adding and Removing Topics

5.1.1 Adding Topics

You might be interested in specific topics which are not frequent enough to appear in the list of topics. You can add these topics to your project manually and Explorer will analyze your data for them with a semantic search.

You can add topics by clicking on the Add Topic button in the top of the working panel. When you click on this button, Explorer considers a new topic area for it at the top of the list. Here you can give a name to your topic, and you can add terms to your topic by writing them in the text box and clicking on Add words. When you are done, click on Remove me after adding words.

Note that the name of the topics are only labels that are assigned to them, and therefore each topic should have at least one term. Terms are the exact words the Explorer actually keeps track of in your data. You cannot add a term which is already included in another topic. The new topics are automatically pinned, so Explorer keeps them in the list regardless of their frequency.  

Keep in mind that Explorer does not add these new topics to your analysis until you have pressed the ‘Explore’ button.

When you add new topics to your project and press Explore, Explorer starts re-analyzing data and it inserts new topics into the list of topics in order of their frequency. Then, you can see the information about related topics, suggestions and statistics for new topics.

Note that Explorer does not remove any topics from the list when you add new ones.

5.1.2 Removing Topics

You can also remove a topic from your analysis if you find it unimportant for your project. Hover over that topic in the working panel and click on cross button to remove the topic.

Explorer will ignore the topic when you remove it from the list, however, note that texts including that topic are still available for inclusion in other topics. The terms including in each topic are saved in the the Ignored Terms section, and you can have them back at any point by clicking on them.

5.2 Grouping and Merging Topics

5.2.1 Select button

When you hover mouse over a topic in the working panel, an upwards arrow appears inside the topic box which is called Select button. You select a topic by clicking on this button. When the topic is selected, the topic blue box turns to orange. You can also deselect the topic by re-clicking on this button.

In Explorer, both merging and grouping should start with selecting a topic. In the following it is explained how we can merge and group topics by Explorer.

5.2.2 Grouping Topics Intro

If you find some topics to be in the same category you can put them in one group. This will give you a more organized working panel. In addition, Explorer will tell you about the sentiment and statistic of the whole group.

For instance, for the hotel reviews, you might like to put all the words about food (e.g. breakfast, coffee, wine, etc.) in one group called Food. Explorer shows you the number of the respondents that have written about food (frequency of the whole group). It also shows you the aggregate sentiments of this group, so it wil let you know if the respondents are positive or negative about food on the whole. How To Group

Grouping topics in Explorer starts with selecting one single topic by clicking on Select button. When you click on this button, a down arrow button will appear in the left side of each topic or group. We refer to this button as Group button. You can place the selected topic into a target group by clicking on this button. By default, the name of the group will be the name of the target topic (or group). You can change this name by re-writing it in the working panel.

The new groups are automatically pinned, so Explorer keeps them in the list regardless of their frequency. Statistics

After re-exploring data by Explorer, grouped topics are inserted into the list of the topics in order of their aggregate frequency (which is the number of distinct texts that include at least one of the topics divided by the whole number of topics). This value can be found in both working and detail panel under the group name. In the detail panel and under each group name, you can also find the aggregate sentiment of that group. The frequencies, sentiments, related topics and suggestions for single topics can be found under their names like before. How to Ungroup

You might want to take a topic out of a group. You can click Undo button at the top or click the ungroup button. Note that the topic you ungroup from a group will be automatically pinned. You should update and save after.

5.2.3 Merging Topics Intro

In case that you find two topics equivalent you can merge them to one single topic. This is mostly the case if the terms in two topics are synonyms or paradigmatic neighbors. For instance, for two topics income and compensation including the terms income and compensation successively, you might merge them to get a single topic including both the terms income and compensation. Merging topics increases the strength of the resulting topic in terms of the number of texts that are included in the topic and therefore it results in preciser sentiments and more informative related topics. How to merge

Similar to grouping topics, merging topics starts with selecting a single topic. As before, you select the topic by clicking on the Select button in the topic box. When the topic is selected, an upwards arrow button will appear at the left side of all other topics. We call this button as Merge button. You merge the two topics by clicking on the Merge button for the second topic. By default, the name of the merged topic will be the name of the first selected topic. You can change this name by re-writing it in the topic box.

5.2.4 Topic Coverage

If you choose to you enable it (section 3.3.2), you can receive details about how much of your data is covered by the topics in your model. This can be useful to help you understand how complete your analysis is.

When the setting is enabled, an extra topic is featured at the end of your topic list. It is always labelled ‘Unclassified’. It shows the number of texts which are not included under your current topic analysis, both as a percentage and as an absolute number. The magnitude of this figure often depends on the characteristics of an individual dataset. It is also possible to get examples of the texts which are unclassified and get a sentiment score for this special topic (in the usual manner). This should aid with an understanding of what data it is which is not currently being covered.

6 Explorer Theme Wheels

As mentioned before, Explorer represents an statistical visualization of your first 6 pinned topics (or groups) by Explorer theme Wheels. At the top of each theme wheel, you can find the name of the corresponding topic. The frequency of the topic can be found in the center of the wheel. The pieces of the wheel are the four most frequent related topics. If you hover over a piece you can see the frequency of the corresponding related topic inside the topic.

Here you can see the theme wheel regarding the topic “room” in hotel reviews. The topic has a frequency of 75.4% in the data and its most frequent related topics are “great”, “clean”, “comfortable”, and “staff” respectively. The frequency of “great” as an related topic of “room” is 39.8%. This means that almost 40% of the people who have mentioned room in their responses have found it great.

7 Explorer Concept Modeler

The Concept Modeler is a tool for additional analytics in Gavagai Explorer. You can define different concepts in Explorer and then analyze your data with respect to them. Same as models, concepts are independent from projects and one concept can be used in different projects. A concept is defined by its including terms. In contrast with topics, the including terms in a concept do not need to be synonyms or semantically similar terms, but they are elements that jointly define that concept. Let’s give an example.

In Hotel Reviews you might define a topic called breakfast, including the terms “breakfast” and “the breakfast”. Each of these terms that appear in a text we will know that the topic breakfast is included in the text. Now consider the two following sentences retrieving from texts:

None of the words “coffee”, “latte”, “croissant” or “mornings” can define the topic breakfast independently; as the case in the first sentence, but a combination of these words can tell us that the text might be related to the concept breakfast; as the second sentence.  

Therefore, the concept breakfast can be defined by set of words like {tea, coffee, latte, bread, cheese, jam, morning,…}. You can start defining the concept by a few most necessary words. Then Explorer will show you more suggestions to be added to your concept.

7.1 How to Create and Edit Concepts

To start creating a new concept or editing the old ones, you can click on My Concepts in Explorer navigation bar to be redirected to concepts page. In this page you can see the list of all of your concepts that you have previously created. To create a new concept, click on Create concept button. You will be then redirected to your new concept’s page. Give a name to your concept by writing it in the “Concept name” text field and select the language of your concept. Add terms to your concept by writing them in “Keywords” text field and add them by pressing the add button. When you are done with adding the most necessary terms for your concept, press the Create Concept (and get suggestions) button to save your concept and get new suggestions.  

7.2 Different Types of Concepts in Explorer

Conceptually, you can define two different type of concepts in Explorer and choose between them for different usage. We refer to these concepts as Topical and Sentiment-Based Concepts. Topical Concepts are those Concepts that define a specific topic or subject while sentiment-based concepts are those concepts that define specific feelings or opinions towards topics. For example, the concept Breakfast that we defined above can be considered as a topical concept. Then you can define a sentiment-based concept like “positivity” and put terms like nice, delicious, tasty, etc. in it. In next section we show how you can measure a sentiment-based concept like “positivity” for a topical concept like Breakfast.

7.3 How to Use Topical and Sentiment-based Concepts in Explorer

When you want to export your project to Excel or CSV, in the report configuration box, you can tell Explorer to analyze your data for the concepts you have already created (for exports see 9 “Export as” Button and Exporting the Result of your Analysis). Here in the configuration box, you have two options for analyzing concepts; target concept and concepts to analyze:

7.4 Creating Concepts from Topics

In Explorer it is possible to create a concept from a topic. When you open a project, in the left panel of the GUI and for a topic of your choice, you can click on Edit terms button which is located next to the blue topic box.

Then, in the top right corner of the grey area, there is a button for creating a concept from your topic. When you click on this button, a confirmation pop up will appear and after you click ok, Explorer creates a concept from your topic. The concept has the same name as your topic and same terms as your topic terms. When your concept has been created, you can choose to either stay in your project page or go to that concept page to edit it.

8 Models in Gavagai Explorer

You can save a project in Explorer as a model and then apply the model as a template for other projects. Using models is a suitable approach when you analyze similar types of data frequently and you have same concerns in your analysis; for example, you are looking for the same topics or themes. Here we explain models in Explorer and we guide you through creating and using them. However, if this is the first time that you are reading this document, we recommend you to skip this section for a little while and return to it after reading other parts of the document.

A model consists of your general Topic structure in the project you are Exporting your Model from; meaning grouped topics, merged topics, pinned groups and topics, and your ignored terms. When you apply a model on a project, the project current model will be replaced by the new one. Therefore, if you are unsure, you would better save the current model first.

Note that to apply a model to your project, the project’ file does not need to have the same specifications as the model’ source file (e.g. same columns or same number of texts, etc.).

8.1 Creating and Applying a Standard Model in Explorer

8.1.1 Create Standard Models from Projects

You can create a model from a project by selecting Standard under the ‘Save Model as’ drop-down menu in the working panel. The Model saving function works much like the save feature in a computer game: the current state of your analysis at the time when you pressed save is stored in the system memory and this state can be applied to any existing project. Once your model has been created, you can navigate to the ‘Models’ page and rename it to something informative so you can keep track of it easily.

8.1.2 Upload Models

You can also upload a model from your computer and use it in different projects. The Upload Model button can be found in Models page. Please note that a file should be in Excel or csv format.

8.1.3 Applying a Standard Model to a Project

Any saved model can be applied to any other project. During the initialisation phase of a new project you can find the model under Apply the model drop-down menu in configuration panel. Or by scrolling up from the main Explorer interface of an existing project, you should be able to find this screen:

Another way of applying a model to your project is to find the ID of another project from the browser’s address bar (the last segment in the URL), and enter it in the provided text field.

8.2 Sharing a Model

It’s also possible to share a model with your colleague so that they can use it in their analysis. Go to Models, click on Share button and insert the e-mail address of the colleague you would like to share the model with. They should receive a notification and there will be a pop-up message in their account saying that a model was shared with them.

8.3 Dynamic Models in Explorer

A Dynamic Model is an extension of a Standard model with some additional functionality. When you save a project’s model in the Dynamic format, this project will control the structure of that Dynamic Model. We refer to the source project as Master Project.

You can apply a Dynamic Model to other projects. We refer to a project which is dependent on a Dynamic Model as a Dependent project. When you make changes to the Master Project, these changes will be automatically applied to its Dynamic Model. All Dependent projects using this model will subsequently be updated to reflect the latest changes. This makes it easy to apply a model to multiple projects and update it across all these projects in a consistent manner.

8.3.1 Creating a Dynamic Model

In Explorer you can create a Dynamic Model from a project by selecting Dynamic under the ‘Save Model as’ drop-down menu in the working panel.

8.3.2 Applying a Dynamic Model to a Project

You can apply a Dynamic Model to a project in the same way as a Standard model (see 8.1.3 Applying a Model to a Project). Every Dynamic Model will be prefixed with ‘(DYN)’ to indicate that it is a Dynamic Model.

On applying a Dynamic Model, the project will become a Dependent project. When you make changes to a Master Project, the connected Dynamic Model will be updated. For each of the projects dependent on the Dynamic Model, you will receive a notification indicating which projects are out of date. You will need to re-explore each project to get the latest version of the model. When you load such a project, the explore button will be active with a reminded that you need to re-explore the project.

8.3.3 Editing the Dynamic Model

After you have created a Dynamic Model, you can find it in the list of models in models page with an indication that the model is Dynamic.

If you are the user who created the Dynamic Model, you can see the previous versions of the model in the model edit page. You can revert the model to an older version by clicking on revert button. This will also revert the corresponding Master and Dependent project accordingly.

In the model edit page, you can also see the list of projects which are dependent on the Dynamic Model.

8.3.4 Dynamic Models with Translation

You can apply a Dynamic Model to a project which has a different language than the Master Project (from which the model was created). In this case, the pinned topics terms in the model are translated to the language of the Dependent project. The topic labels, however, are retained in the language of the Master Project, to simplify cross referencing.

When the dynamic model is applied to a different language (or when a new term is added to the dynamic model), we try to translate the words from the source language (that first project you created the dynamic model from) to the destination language (the project that you applied the dynamic model to). We consider all translations and try to pick the most frequent one in the destination dataset and use that as a topic term. We also consider the source term as a possible candidate for the translation.

If none of the translation candidates exist in the destination data set, we use the one of the candidates and expect the end users to translate the source term to the destination language. If there are no translations available at all, we use the source term as the translation - again, we expect the end users to translate the source term to the destination language.

8.3.5 Editing the Translations in a Dependent Project

If your Dynamic Model has been applied to a project in another language, it is possible to edit the translations of the topic terms by removing translated terms or adding new translations. This is useful if a mistranslation has occurred, or if there are several variations of a translation you would like to add. Variations can be different forms of the same word or simply different ways of saying the same thing in the target language.

To remove a translated term from a Dependent Model, simply navigate to the relevant topic edit screen and click on the unwanted term to remove it.

When you add a new term to a topic in a Dependent Model, you must link it as a translation of a topic term in the original Master Model. This is to insure that any changes to the structure of the Master Model are correctly applied to the structure of the Dependent Models.

Adding an extra translation to a topic in the Dependent Model can be done in the usual way. You can either accept a suggestion in the topic of a downstream model or add a term manually in the free text box. Once you add a translation, you will get a pop up for you to choose a term in the Master Model to link it to.

If terms are moved into a different topic or removed from a topic in the Master Model, the linked translations in the corresponding Dependent Models will follow suit.

It is also possible to add and remove terms in the “Ignored Terms” box in a Dependent Model. These terms do not have to be linked to a translation in the Master Model (if they reflect something specific to the new target language, for example).

8.3.6 Sharing a Dynamic Model with Another User

You can share a Dynamic Model in the same way as a Standard model (see 8.2 Sharing a Model). The user to whom the Dynamic Model has been shared will see an indication that the model is a Dynamic Model.

8.3.7 Disconnecting a Dynamic Model

If you have connected your project to a Dynamic Model you can disconnect your project from it in the project toolbox page. Locate the connected dynamic model at the bottom of the page and click on the ‘Disconnect Model’ button.

If the project you are disconnecting from the Dynamic Model is the Master Project (ie, the project that the Dynamic Model was created from), all other projects dependent on this Dynamic Model will be disconnected from it as well and will no longer receive any updates made to the Master Project. The Dynamic Model will still be available in the Models page as a Standard Model.

If the project you are disconnecting from the Dynamic Model is a Dependent Project (ie, a project that the Dynamic Model was applied to and the project receives updates from the Dynamic Model), the project will no longer receive any updates made to the Dynamic Model or the Master project and can be edited as though it was a normal project. All other projects connected to the dynamic model will be unaffected.

8.3.8 Deleting a Dynamic Model

If you delete a Dynamic Model, its corresponding Master Project and all Dependent projects will be disconnected from the model. Note that the Master Project and Dependent projects will still have the latest version of the Dynamic Model but now you can work with these project as regular projects.

8.3.9 Deleting a Master Project

If you delete a Master Project, its connected Dynamic Model will be disconnected from the project (the model gets converted to a Standard model) and all Dependent projects will also be disconnected from the Dynamic Model and will become like regular projects. So you can work with them as usual.

8.3.10 Dynamic models and Auto-Add Terms

As described above, when a Dynamic Model is applied to a project, there is a very strong connection or association between master project and dependent project: for every new term added in the dependent project, the user has to add translations to specify which term in the master project each translation corresponds to. The Auto Add Terms functionality on the other hand is designed so that terms are automatically added to a project, without the user’s intervention. Given that these two functionalities are contradicting, we do not allow auto add terms to be added to dependent project and auto add terms are not included in the dynamic model itself, even if the master project does have one or more auto add terms.

If the master project of a Dynamic Model contains an Auto Add Term, new “n-gram” topic terms which are added to the master project and the in turn the Dynamic model, as data is appended to the Master Project. These new n-gram topic terms are subsequently propagated (and optionally translated) to the dependent projects.

9 “Export as” Button and Exporting the Result of your Analysis

After each iteration and before you make any changes to your project, Explorer provides a Save as button where you can save the current result of your analysis in different formats. Under Export as drop-down menu there are 4 options; PDF, Excel, Full CSV and Model.

If you want to have a brief report of your analysis similar to what you see in Explorer GUI, you can save your project as either PDF or Excel. In case you want to have a detailed result of your analysis with respect to individual texts, you can choose Excel or Full CSV.

When you choose the format, a message box will pop up (next figure). You can either choose to be redirected to the Project Settings page (see 3.3.2 Project Settings) where you can download your file or you can do it later by pressing Ok. The Project Settings page can be accessed from Explorer homepage (My Projects page) by clicking on cog button for that Project as on the figure below, or through the Secondary Menu inside the Project.

9.1 Save as Excel and Full CSV

If you select an Excel or CSV format, then you have the option to configure a number of extra parameters in your report.

Topics can be sorted alphabetically or by frequency (default option). Keywords can be included or not. You can choose which concepts to analyze and which topics to count sentiment for. A target concept for the whole report can also be set.

When you download the result of your analysis, aside from the summary tab, you you also will have a data tab containing your original data appended by the analysis result from Explorer. Since all cells are handled as text, eventual metadata specifying the cell type is lost as well as any eventual formulas existing in the original excel file. If the meta-type of a number column is important for you then just export the report in a CSV format and use the import function in excel to create an excel version of the report. Excel normally interprets the text containing a number as a number type, given it is according to the format of the excel application importing the CSV file. Explorer performs a row based analysis of your data in respect to your pinned topics, target concepts and sentiments, and for each text it adds the result of the analysis to the corresponding row. In the following we explain the appended columns.

9.1.1 Pinned Topics

The first added columns are the columns regarding to your pinned topics. You can see the name of each pinned topic in a header of a column. A cell in a topic column contains 1 if the corresponding text includes that topic, and 0 otherwise.

9.1.2 Sentiments

The next added columns are the ones containing sentiment scores. Explorer measures 8 standard sentiments for each text; they are: SENT: DESIRE, SENT: FEAR, SENT: LOVE, SENT: POSITIVITY, SENT: SKEPTICISM, SENT: NEGATIVITY, SENT: HATE, SENT: VIOLENCE. If you have Neutral sentiment feature on (see 3.3.2 Project Settings), then there will be a column for NEUTRAL as well. You can see their names on the header of the columns. Each cell in a sentiment column contains the score of that sentiment for the entire corresponding text. In case you have selected a Target Concept for the analysis of your project, the sentiment scores will be restricted to the sentences that contain at least one of the terms in any of those concepts.

9.1.3 Target Concepts

The next columns are the target concepts that you have chosen to include in the report. The Explorer will append two new columns for each target concept. For each target concept, there is a column in the Excel file having the concept name in its header. Each entry in a concept column is the number of distinctive terms in that concept that are included in the corresponding text. The other column is the sentences that appeared in the corresponding text.

9.1.4 Concept to Analyze

You can select your sentiment-based concepts under “Concepts to Analyze”. In this case, Explorer computes the strength of each concept for each text and shows them to you in new columns, one for each concept. Here the computation is performed in the same way as for Explorer built-in sentiments (see 4.3.2 The Sentiment Scoring System) which will always use aggregated settings (as opposed to binary settings).

9.1.5 Keywords

The next columns in the report are the keywords columns. Keywords are those terms that best describe the subject of the texts. There are 4 different keywords columns in the Excel report:

9.1.6 Sentiments Per Topic 

For a selected topic, you can gauge the sentiment in each individual text and add a column for each sentiment to the report. For instance, if you select a topic after clicking the Export as Excel or CSV dialogue box to bring up the Configure Report Box, each text in your project will be analyzed with respect to the Positive, Negative, and Skepticism sentiment around the topic, and the columns for the sentiment scores of the specific topic(s) chosen will be added to the resulting Excel file under the name Sent: Positivity(topic name).

This is useful when you want to compare the positive sentiment score of the verbatim – located in the usual column called SENT: POSITIVITY. With the topic’s positive sentiment score – located in the new appended column called SENT: POSITIVITY (what ever topic you chose). For example, if your topic’s positive sentiment score is very large and almost as large as your verbatim sentiment score. Then there is a strong indication that the topic is driving the positivity of the verbatim.

*note adding a lot of topics to analyze will add thee columns per topic. Thus, the report generation time will be affected, the more topics are selected.

The Sentiments per topic and per verbatim are used in the Driver PDF report. 

9.2 Drivers report PDF 

Satisfaction Drivers is a new form of report on the base of Explorer’s best features that provides an effortless and efficient “one click” analysis highlighting satisfaction drivers in survey responses, reviews, etc. It’s quick, easy, and doesn’t require any additional user input.

In the other words, it gives a direct answer to the question “What drives respondent satisfaction?”.

The “one click” functionality is accessed directly from the Explorer start page. After an Excel file with at least one text column is uploaded, the project is created and explored. It is also possible to generate a Satisfaction Drivers report by choosing “Driver (PDF)” in the “Save as” drop down menu in your existing projects.

The report will show a simple and neat visualisation of a handful selected drivers from the given datafile - both positive and negative (if applicable).

The axes of the diagram are: Y axis - correlation to overall satisfaction, X axis - driver satisfaction ratio (in %). Each bubble represents one driver, based on the analysis of topics and their related topics. The drivers’ position on the diagram represent their importance and meaning for the customer satisfaction.

Top drivers: The average net satisfaction rate for a driver. Both positive and negative sentiments are used to judge the satisfaction of a driver. Ratio: The ratio of texts that contain a driver where the average net satisfaction rate is positive/negative. Correlation: The correlation between driver sentiment and overall sentiment. Occurrence: The amount of texts in that the satisfaction driver occurs.

9.3 PDF 

This is a PDF report that mirrors the web application. It will show the Groups, Topics, Terms that build up a Topic, and the Related topics for each Topic. This is useful way to share the report.

10 Sentiment Customizations

Gavagai Explorer uses a lexical approach to perform sentiment analysis. The system uses a curated list of sentiment bearing terms for each sentiment (the sentiment’s lexicon), to determine the sentiment for verbatims. To ensure that the sentiment analysis performed by the system is usable across domains, the lexicon for each of the sentiments is generic, and contains non-ambiguous terms. For example, the term ‘soft’ has no sentiment inherently, however in the context of diapers it is a positive term and in the context of teeth, it is a negative term. To ensure that the analysis of neither data set is negatively impacted by the term ‘soft’ being classified as positive or negative, the term is excluded from the lexicon of both sentiment. However, for analysis of a dataset of diaper reviews, it would be appropriate to classify ‘soft’ as positive specifically for that dataset. This can be achieved by utilizing Sentiment Customizations.

10.1 List of Sentiment Customization

The list of your Sentiment Customizations can be accessed from the top menu by clicking the tab ‘SENTIMENTS’ on the top menu. Here you will be presented with a list of your Sentiment Customizations, with a possibility of deleting the Customizations. Clicking any of the Customizations will allow you to edit it. You can also choose to create a new Sentiment Customization by clicking the ‘Add Customization’ button.

10.2 Creating or editing Sentiment Customizations

In the edit Sentiment Customization screen you can edit the title of the Customization and add or remove terms from the lexicons of each of the 8 supported sentiments used by Gavagai Explorer. Please note that the language of the Sentiment Customization must be selected prior to adding or removing terms and once selected it cannot be changed.

10.2.1 Adding Sentiment Terms

You can add custom sentiment terms to the lexicon of a sentiment utilized by the Gavagai Explorer for performing sentiment analysis. When a sentiment term is added to a sentiment, it is an indication that when the added term is encountered when performing sentiment analysis in the context of a project where the Customization is being utilized, the term is to be treated as part of the sentiment’s lexicon.
For example, if the term ‘soft’ is added to the sentiment ‘POSITIVITY’ in a Sentiment Customization, and that customization is used while exploring a project, when the system encounters the term ‘soft’, it will be treated as a positive term when performing sentiment analysis. Weights for Added Sentiment Terms

If you are an advanced user of Gavagai Explorer and are aware of the details of the sentiment algorithms, you may choose to include/override weights for relevant sentiment term, by adding a ‘::’ followed by the weight after the term. For example, if you would like to include the term ‘softer’ with weight 1.2, you may do so by entering softer::1.2 in the text input. If the weight for any of terms is different from the default, it will be displayed when the term is hovered over.

10.2.2 Removed Sentiment Terms

You can also choose to exclude sentiment terms from the lexicon of a sentiment utilized by the Gavagai Explorer for performing sentiment analysis. When a term is removed from a sentiment, it is an is an indication to the system that when the term is encountered in a project where the customization has been applied, the term is not to be treated as a part of that sentiment’s lexicon when performing sentiment analysis (assuming that the term was originally part of that sentiment’s lexicon). If the term was not part of the sentiment’s lexicon to being with, the term is no impact to adding the term.

For example, if the term ‘beast’ is removed from the sentiment ‘NEGATIVITY’ in a Sentiment Customization, and that customization is used while exploring a project, when the system encounters the term ‘beast’, it will not be treated as a positive term when performing sentiment analysis.

10.2.3 Terms belonging to other Sentiments

In some cases, you may want to the system to treat a term as part of a sentiment’s lexicon that it is not generally associated with. For example, the term ‘cold’ is generally considered a negative term, but not in the context of ice cream, in which case it is considered positive To apply this customization to your data set, you need to create a Sentiment Customization in which ‘cold’ is part of the the ‘Added Sentiment Terms’ for POSITIVITY and also part of the ‘Removed Sentiment Terms’ for NEGATIVITY.

It may be hard to determine which other sentiments a term belongs to when adding it to the ‘Added Sentiment Terms’ of a specific sentiment. To assist with this, Gavagai Explorer will notify you if the added term belongs to the standard lexicon of any other sentiment. Based on this information, you may choose to add the term to the ‘Removed Sentiment Terms’ list of that sentiment.

10.3 Applying a Sentiment Customization to a project

Once your sentiment customization is ready and you want to use if for sentiment analysis of a project, you may do so by choosing the Sentiment Customization in the ‘Sentiment Customizations’ tab of the project in the project explore page. Using a Customization when exploring the project will cause all related Sentiment Analysis to utilize this Customization. This includes Project & Topic Level Sentiments, sentiments in Excel and CSV reports, sentiments in Examples, and in the Gavagai Analysis Dashboards.

The list of Sentiment Customizations available for a project is filtered based on the language of analysis for the project. The default Sentiment Customization selected is the ‘Use Standard Sentiments’ option.

11 Payment System

Gavagai Explorer offers two different types of payment methods: credit card payments and invoicing. When your Trial period has ended, login to your account to start your subscription. You will be prompted to choose the plan you want to start the subscription on. If you have not yet entered your credit card information you will be asked to add a credit card while reactivating your subscription. Alternatively, you can contact our customer support, so that we can set up invoice payments for you.

11.1 Plans

We base our account plans on a subscription format with sets of features that grow more advanced with higher levels. You can switch to a plan which is more appropriate to your exact needs at any time.

More specific details of the account plans can be found on the account page in the app.

11.2 Credits

Each row in your uploaded file corresponds to one credit charged to your account. As soon as you hit the ‘Explore’ button for the first time for a given project, the corresponding credits will be deducted. This means that if there is an error with your file during the upload process, you won’t be charged, as you have not been able to proceed to the exploration phase.

With each plan we include a number of credits each month. If you need more credits for exploration you can purchase them on your account page. As long as your account is active (the monthly subscription cost is paid), the credits purchased do not expire. The Explorer ‘Enterprise’ plan provides the possibility to have a custom number of monthly credits included with the subscription.

You can check the number of remaining credits you have on the “Account” page.

11.3 Project Slots

Every account plan has a fixed number of project slots that you are allowed to use. If you require more or less project slots you can downgrade or upgrade you account on the Account Page to a plan with an appropriate number of slots. You can also view the number of available project slots in your account on the “Account” page.

11.4 Upgrading or Downgrading Plans

To start the process of upgrading or downgrading, first, log in to your account and then go to the account page. If you have not yet entered your credit card information you need to do so before you upgrade or downgrade because you may be charged for the upgrade. If your account is set to invoice billing you do not have to enter your credit card information. Then click ‘Change Product Plan’ and subsequently choose the plan to which you want to upgrade or downgrade.

11.5 Charging for Upgrades and Downgrades

When you upgrade your plan you will have to pay the extra cost of the plan for the remainder of the billing period. For example: if you upgrade from Subscription 10 to Subscription 20 and there are 5 days left in the current billing period here is how we calculate the extra charge: Your extra charge will be the difference in price between the Subscription 10 and the Subscription 20 plan times the ratio of the time left in the period which is 5 days so the extra charge will be the plan difference times 5/30. When you downgrade you will not be charged or credited. The actual downgrade will happen at the end of the billing period so you can keep using the current features until then.

11.6 Cancelled Accounts

If you decide to cancel your plan completely, you can click the ‘Cancel Account’ button and your account will be deactivated at the end of your billing cycle. To activate the account again, log in, add payment information and select a plan.

Please note that if you do cancel your account, all your projects will be deleted four months after the cancellation has taken effect.

12 Managed Users and Teams

Each user should have their own account since the Gavagai Explorer does not support concurrent analysis of projects. The manager and managed users feature, simplifies and enables co-ordination within a team during analysis. By adding managed users to a manager account you can collaborate and share your credits with them. This way, all of your colleagues or partners can use their own Explorer accounts, but only the manager account will be charged. There are other features that aid teams in cooperating during analysis of projects, the details of which can be found on the plans page.

12.1 Inviting Users

You can invite other users via the Account page. Scroll down until you see the following, and click on “Invite user” to invite another user to be managed by you. This user will then get a notification via e-mail and can accept the invitation when they log into their account. When they accept the invitation they will also join the plan which the manager is subscribed to.

12.2 Disconnecting Managed Users

On the same page, you can also remove managed users.

12.3 Disconnecting from Manager

As a managed user, you can go to your Account page at any time and disconnect from your manager.

13 The API

If you are a developer and want to access Explorer’s API, you can do so with the credentials from the Explorer web application. You don’t need anything else, so no API key, etc.

Here you find the developer documentation: Gavagai Explorer API Documentation

For a tutorial on how to get started with the API, refer to this page: Gavagai Explorer API Tutorial