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Extracting Appraisal Expressions from Short Texts

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Web-Age Information Management (WAIM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9098))

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Abstract

Short texts such as tweets and E-commerce reviews can reflect people’s opinions on interested events or products, which are much beneficial to many applications. However, one opinion word may have different sentiment polarities when modifying different targets. Therefore, in this paper we propose to extract “appraisal expressions” that are represented by tuples of (opinion word, target), indicating an opinion word and the target modified by the word. By extracting appraisal expressions, we can further construct target-sensitive sentiment dictionaries and improve the effectiveness of sentiment analysis on short texts. Consequently, we propose a filtering-refinement framework to extract appraisal expressions from short texts. In the filtering step, we extract appraisal-expression candidates, and in the refinement step, we use SVM to extract appraisal expressions and present a dependency-grammar-based approach to automatically label training data. Comparative experiments between our proposal and three baseline methods suggest the superiority and effectiveness of our proposal.

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Correspondence to Peiquan Jin .

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Jin, P., Yu, Y., Zhao, J., Yue, L. (2015). Extracting Appraisal Expressions from Short Texts. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-21042-1_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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