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International Conference on NLP

JapTAL 2012: Advances in Natural Language Processing pp 278–288Cite as

Twitter Sentiment Analysis Based on Writing Style

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7614)

Abstract

This paper proposes a new method of sentiment analysis for Twitter. Tweets contain various expressions; e.g., use of emoticons. The usage of these expressions links to the user’s identity and individual characters. Handling these characteristics is useful for the sentiment analysis. We focus on writing styles of each user. In this paper, we define three types of writing style; formal and two informal expressions. First, our method classifies each tweet into the three types. Then, it generates classifiers for each writing style. We apply our method to a positive / negative classification task of tweets. In the experiment, the accuracy of our method increased by approximately 3 points as compared with some baseline methods.

Keywords

  • Sentiment analysis
  • Positive/negative classification
  • Writing style

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  • DOI: 10.1007/978-3-642-33983-7_28
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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Iizuka, Fukuoka, 820-8502, Japan

    Hiroshi Maeda, Kazutaka Shimada & Tsutomu Endo

Authors
  1. Hiroshi Maeda
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  2. Kazutaka Shimada
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  3. Tsutomu Endo
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Editor information

Editors and Affiliations

  1. Information and Media Center, Toyohashi Universtiy of Technology, 1-1 Hibarigaoka, Tenpakucho, 441-8580, Toyohashi, Japan

    Hitoshi Isahara & Kyoko Kanzaki & 

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Maeda, H., Shimada, K., Endo, T. (2012). Twitter Sentiment Analysis Based on Writing Style. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_28

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  • DOI: https://doi.org/10.1007/978-3-642-33983-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33982-0

  • Online ISBN: 978-3-642-33983-7

  • eBook Packages: Computer ScienceComputer Science (R0)