Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity preferences of verbs are extracted on the basis of a large and dependency-parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas the upper bound goes up to 92%.