Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Event matching using the transitive closure of dependency relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Sentiment classification using automatically extracted subgraph features
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Voice of the customers: mining online customer reviews for product feature-based ranking
WOSN'10 Proceedings of the 3rd conference on Online social networks
Modeling of stylistic variation in social media with stretchy patterns
DIALECTS '11 Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties
Automatic generation of short informative sentiment summaries
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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In this paper we present a novel approach to categorizing comments in online reviews as either a qualified claim or a bald claim. We argue that this distinction is important based on a study of customer behavior in making purchasing decisions using online reviews. We present results of a supervised algorithm for learning this distinction. The two types of claims are expressed differently in language and we show that syntactic features capture this difference, yielding improvement over a bag-of-words baseline.