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Expanding Sentiment Lexicon with Multi-word Terms for Domain-Specific Sentiment Analysis

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Digital Libraries: Knowledge, Information, and Data in an Open Access Society (ICADL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10075))

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Abstract

The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.

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Authors and Affiliations

  1. Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore

    Sang-Sang Tan & Jin-Cheon Na

Authors
  1. Sang-Sang Tan
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  2. Jin-Cheon Na
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Correspondence to Sang-Sang Tan .

Editor information

Editors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Atsuyuki Morishima

  2. Vienna University of Technology, Vienna, Austria

    Andreas Rauber

  3. Victoria University of Wellington, Wellington, New Zealand

    Chern Li Liew

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Tan, SS., Na, JC. (2016). Expanding Sentiment Lexicon with Multi-word Terms for Domain-Specific Sentiment Analysis. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_34

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  • DOI: https://doi.org/10.1007/978-3-319-49304-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49303-9

  • Online ISBN: 978-3-319-49304-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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