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    Feature subsumption for opinion analysis

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    Lexical features are key to many ap- proaches to sentiment analysis and opin- ion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico- syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and perfor- mance. We use the subsumption hierar- chy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing un- necessary features. We show that reduc- ing the feature set improves performance on three opinion classification tasks, espe- cially when combined with traditional fea- ture selection.

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    Description

    Title : Feature subsumption for opinion analysis
    Author(s) : Ellen Riloff, Siddharth Patwardhan, Janyce Wiebe
    Abstract : Lexical features are key to many ap- proaches to sentiment analysis and opin- ion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico- syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and perfor- mance. We use the subsumption hierar- chy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing un- necessary features. We show that reduc- ing the feature set improves performance on three opinion classification tasks, espe- cially when combined with traditional fea- ture selection.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2006

    Affiliations IBM T. J. Watson Research Center
    Journal : Proceedings of the 2006 Conference on Empirical Methods in Natur
    Volume : 42
    Publisher : Association for Computational Linguistics
    Pages : 440
    Url : http://portal.acm.org/citation.cfm?doid=1610075.1610137
    Doi : 10.3115/1610075.1610137

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