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.
The Gavagai Explorer is based on vast knowledge about language use on the Internet. Over the years, the system has picked up information about common terms, their semantic neighbors, and their inflections and variants from billions of texts in dozens of languages. This information is continuously integrated with the way our users interact with the Explorer when modeling and analyzing their data, and thus help provide a user-controlled and light-touch guidance of the analysis of unstructured texts.