Identifying sources of opinions with conditional random fields and extraction patterns

Yejin Choi, Claire Cardie, Ellen Riloff, Siddharth Patwardhan
2005 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05   unpublished
Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001 ) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a
more » ... ce tagging task, Au-toSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79.3% precision and 59.5% recall using a head noun matching measure, and 81.2% precision and 60.6% recall using an overlap measure. 1 In related work, we investigate methods to identify the opinion expressions (e.g., Riloff and Wiebe (2003), Wiebe and Riloff (2005), Wilson et al. (2005)) and the nesting structure of sources (e.g., Breck and Cardie (2004) ). The target of each opinion, i.e., what the opinion is directed towards, is currently being annotated manually for our corpus.
doi:10.3115/1220575.1220620 fatcat:gh32j6j2bzgwxi4vbqpg2vjb6i