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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.

'Export as' option

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.

Sample Excel file

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 Project Settings - Reports) 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.

Settings and reports button

Settings and reports tab

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.

Configure 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.

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.

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 Project Settings - Sentiments), 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.

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.

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).

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:

  • The first column is “Keywords”. Explorer identifies the most significant keywords in each text and inserts them in the corresponding entry of this column in form of [keyword 1, keyword 2,…,keyword n] where the value of n can be different for different texts.

  • The second column is “Qualified Keywords”. Each entry in this column is a list containing those keywords from the previous column (means the “Keywords” column) that a Wikipedia page exists for them. Explorer considers these keywords as qualified keywords.

  • The third column is “Expanded Keywords”. Each entry in this column is a list containing the semantically similar words to the keywords of the corresponding text.

  • The fourth column is “Qualified Expanded Keywords”. This column contains those terms from previous column that have been qualified by Wikipedia as before.

  • The last column is “Summary”. This column contains the most significant sentences in the texts.

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.

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.

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.