Michael Anderson, PhD, JMP Systems Engineer, SAS
Chris Gotwalt, PhD, JMP Director of Statistical Research and Development, SAS
JMP 13 introduced Text Explorer. The new platform provides the ability to curate free-form text and generate insights into themes and important terms. While incredibly useful, this is really only a first step in answering the problem at hand. Often what one really wants to do is identify the keywords in a set of documents that are strongly associated with a particular response, such as purchasing behavior or customer reviews. Typically this is done using traditional sentiment analysis, which relies on word lists supplied by third-party vendors that do not take into consideration the specific context you are working in or the population of people you are interested in understanding. An alternative approach, sometimes called supervised learning sentiment analysis, combines text analysis with predictive modeling to determine which words and phrases are most relevant to the specific problem at hand. It uses data to determine both the direction and strength of the association of the terms in the documents with the response via a fairly approachable modeling exercise. Using JMP Pro 13, supervised learning sentiment analysis is now easier than ever, and we will demonstrate this with a series of case studies arising from consumer research and social media contexts.
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