The goals of subjectivity analysis are to extract opinions, sentiments, and emotions expressed in natural language discourse and to recognize their components and properties. This is currently a very active area of research in natural language processing, with the potential to develop tools supporting information analysts in governmental, commercial, and political domains who want to automatically track attitudes and feelings in the news and on-line forums. How do people feel about the latest camera phone? Is there a change in the support for the new Medicare bill? A system able to automatically identify and extract opinions and sentiments from text would be an enormous help to someone sifting through the vast amounts of news and web data, trying to answer these kinds of questions. In this talk, I will describe a corpus annotated with rich information about opinions and sentiments. I will also present recent experiments using that data to develop and evaluate an automatic system for recognizing the "contextual polarity" of expressions, i.e., whether a phrase is being used to express a positive or negative sentiment, considering the context in which it appears.
Theresa Wilson is a Ph.D. candidate in the Intelligent Systems Program at the University of Pittsburgh, where she previously earned an M.S. in computer science. Her research investigates extracting opinions, sentiments, and emotions expressed in natural language discourse and recognizing their components and properties.
This talk is part of the CLIP Colloquium Series, organized by Jimmy Lin (jimmylin -at- umd .dot. edu). For the complete schedule, please visit http://www.umiacs.umd.edu/research/CLIP/colloq/.