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Discourse Network Analyzer (DNA) is a Java-based application for qualitative category-based content analysis. It serves two purposes: coding statements of actors into categories, and converting these structured data into networks that are readable by UCINET, visone and other network-analytic software packages. The software can extract either bipartite (affiliation) networks or adjacency networks. It is complementary to semantic network analysis applications because it neither relies on algorithms for automatic text processing, nor does it focus on the internal meaning or mental representation of a single text or document. Instead, tags are applied to the text data by manual inspection, thus rendering it more flexible, yet at the same time more work-intensive. DNA can be used to code a large body of text documents and then convert them into graphs. The application is currently being developed, tested and heavily used in my dissertation research project on German pension politics. Updates will be posted here as soon as something has been published. If you use DNA, I would love to hear from you about your project (if possible, via the DNA-help mailing list). For more information about the software, please consult the documentation or obtain a free copy from the download page.

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

Sentiment Analysis is a terribly difficult problem. The problem is in defining the problem - the input is the problem - but you can't avoid it, IT IS the future, and I'm very optimistic about it! Professor Bing Liu started off the Sentiment Analytics Symposium yesterday with the statement above and I couldn't agree more. Subsequently he gave the pre-workshop audience a detailed 3.5 hour overview of the state of text analytics. It was not surprising to me that almost a quarter of the audience were young developers (MacBook Pro in hand), with the hopes to learn how to incorporate their own sentiment analysis engines into their business applications.

See relationships among data points: Network Diagram; Scatterplot; Matrix Chart Compare a set of values: Bar Chart; Block Histogram; Bubble Chart Track rises and falls over time: Line Graph; Stack Graph; Stack Graph for Categories See the parts of a whole: Pie Chart; Treemap; Treemap for Comparisons Analyze a text: Word Tree; Tag Cloud; Word Cloud Generator; Phrase Net See the world: Massachusetts Map; World Map; US County Map; New Jersey Map

Third Annual  JITP Conference, May 16 & 17, 2011 - UW, Seattle, WA   Early Registration Period: Register by March 20, 2011 to save $50 Students $50, Others $75