Paper: Liars and Saviors in a Sentiment Annotated Corpus of Comments to Political Debates

ACL ID P11-2099
Title Liars and Saviors in a Sentiment Annotated Corpus of Comments to Political Debates
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We investigate the expression of opinions about human entities in user-generated con- tent (UGC). A set of 2,800 online news comments (8,000 sentences) was manually annotated, following a rich annotation scheme designed for this purpose. We con- clude that the challenge in performing opi- nion mining in such type of content is correctly identifying the positive opinions, because (i) they are much less frequent than negative opinions and (ii) they are par- ticularly exposed to verbal irony. We also show that the recognition of human targets poses additional challenges on mining opi- nions from UGC, since they are frequently mentioned by pronouns, definite descrip- tions and nicknames.