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Emoticon-Based Emotion Analysis for Weibo Articles in Sentence Level

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11248))

Abstract

In this study, we propose a multi-label emotion study for Weibo articles in sentence level based on word and emoticon feature. We crawl articles from Weibo randomly, and extract sample sentences based on word emotion lexicon which has been constructed from a Chinese emotion corpus (Ren-CECps). Two machine learning methods including Support Vector Machine and Logistic Regression are employed to conduct the emotion classification experiments with the word feature and the combination feature of words and emoticons respectively. The significantly improved results given by classification experiments with the emoticon feature prove the effectiveness of taking emoticons in emotion analysis.

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Notes

  1. 1.

    http://a1-www.is.tokushima-u.ac.jp/member/ren/Ren-CECps1.0/DocumentforRen-CECps1.0.html.

  2. 2.

    http://open.Weibo.com/wiki/API.

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Authors and Affiliations

  1. Faculty of Engineering, Tokushima University, Tokushima, Japan

    Yunong Wu, Xin Kang, Kazuyuki Matsumoto, Minoru Yoshida & Kenji Kita

Authors
  1. Yunong Wu
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  2. Xin Kang
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  3. Kazuyuki Matsumoto
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  4. Minoru Yoshida
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  5. Kenji Kita
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Corresponding author

Correspondence to Yunong Wu .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Maha Sarakham, Thailand

    Manasawee Kaenampornpan

  2. Center for Computing Technologies, University of Bremen, Bremen, Germany

    Rainer Malaka

  3. Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Duc Dung Nguyen

  4. AIST Tokyo, Tokyo, Japan

    Nicolas Schwind

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Wu, Y., Kang, X., Matsumoto, K., Yoshida, M., Kita, K. (2018). Emoticon-Based Emotion Analysis for Weibo Articles in Sentence Level. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-03014-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03013-1

  • Online ISBN: 978-3-030-03014-8

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