Unstructured text data is messy. Data scientists working in text analytics know cleaning data is time consuming. Users of DiscoverText build reusable custom machine classifiers or “sifters” to find the most relevant items before sorting them into topics and sentiment categories. DiscoverText combines data science methods with e-discovery text analytics tools to shorten a process that used to last weeks or months; our machine-learning sifters are created in hours or just a few minutes using crowdsourcing. We support technical integrations with Twitter and SurveyMonkey. Academics trust DiscoverText to help them do better, more transparent research, resulting in more publications.
Keyword search, word clouds, and topic dictionaries are power tools for text analytics
Discover central topics and also elusive but valuable unexpected or rare concepts. Use this information to train machine-learning classifiers to recognize relevant text and social media data. Jump into data using an interactive word CloudExplorer or build a mini topic dictionary using “defined” search.