Computational Models of Discourse Analysis

1. Computational Models of Discourse Analysis Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute

2. Warm Up • How would you rate the new girl and the Indian blogger on these scales? • An why?

3. Warm-Up discussion • We read two theory papers so far in this unit: • The first paper was about what style of reference says about identification with a community and with an interlocutor (as an ingroup member or not) • The second paper related to the way the use of time in a narrative speaks about self-concept and projected reader • How do these issues related to the aspects of personality covered in the Gill paper?

4. Based on that comparison, do these numbers make sense?

5. Further Discussion • What was the research question the authors were trying to answer? • What was the reason for choosing LIWC? • Do you think this was a reasonable approach? • What do you conclude about how much of personality as it is revealed through text is captured by LIWC features? • Notice that the author cited a lot of prior work from his own lab or people who used a similar methodology

6. Background on LIWC • Developed by Pennebaker • Used frequently in medical informatics • Usually applied to highly controlled data • Isolates as much as possible the variable being examined • Is the blog data controlled in the right way? * Connection with subpopulations/ domain adaptation

7. Online LIWC Assessment

8. What would we conclude? • The new girl is more neurotic • The Indian is a little more extroverted • The Indian is a little more open • The new girl is more conscientious • The new girl is more agreeable

9. * Would you expect a machine learning model with these features to work well?

10. What features might you try instead?

11. Announcement For Monday (pp 92-135) Analyze one of the two blog posts from this perspective

13. Questions?