Overview of WordStat</p
WordStat is a text analysis module specifically designed to study textual information such as responses to open-ended questions, interviews, titles, journal articles, public speeches, electronic communications, etc. WordStat may be used for automatic categorization of text using a dictionary approach or various text mining methods. WordStat can apply existing categorization dictionaries to a new text corpus. It also may be used in the development and validation of new categorization dictionaries. When used in conjunction with manual coding, this module can provide assistance for a more systematic application of coding rules, help uncover differences in word usage between subgroups of individuals and assist in the revision of existing coding using KWIC (Keyword-In-Context) tables. WordStat includes numerous exploratory data analysis and graphical tools that may be used to explore the relationship between the content of documents and information stored in categorical or numeric variables such as the gender or the age of the respondent, year of publication, etc. Relationships among words or categories as well as document similarity may be identified using hierarchical clustering and multidimensional scaling analysis. Correspondence analysis and heatmap plots may be used to explore relationship between keywords and different groups of individuals.
This tutorial demonstration will show some of WordStat content analysis and text mining capabilities on a sample data file. The data file consists of 243 speeches from the 2008 US presidential campaign.