The Linguistics of Spin

There has been a great deal of recent research on the computational analysis of texts in order to identify sentiment; that is, to determine automatically whether a text expresses a positive or negative perspective or opinion on a topic. Most such work focuses on explicitly expressed opinions, e.g. automatically labeling movie reviews as favorable or unfavorable. In this talk, we will focus on a less studied problem: the computational analysis of implicit sentiment, or spin, in text. We identify underlying semantic properties of statements that predict perceptions of sentiment, and we show how observable syntactic reflexes of those properties can be used, fully automatically, to accurately label a text as expressing a positive or negative attitude toward its topic, even in the absence of overtly opinionated language.

Speakers

Stephan Greene
PhD CandidateDepartment of LinguisticsUniversity of Maryland

Stephan Greene is a recent Ph.D. graduate of the UMD Department of Linguistics, and works at ATG, a leading provider of Web marketing and e-commerce software.

Philip Resnik
Associate ProfessorDepartment of Linguistics and the Institute for Advanced Computer StudiesUniversity of Maryland

Philip Resnik is an associate professor at UMD with joint appointments in the Department of Linguistics and the Institute for Advanced Computer Studies.