Sentiment analysis in twitter: From classification to quantification of sentiments within tweets

Mondher Bouazizi, Tomoaki Ohtsuki

研究成果: Conference contribution

14 引用 (Scopus)

抜粋

Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.

元の言語English
ホスト出版物のタイトル2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509013289
DOI
出版物ステータスPublished - 2017 2 2
イベント59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
継続期間: 2016 12 42016 12 8

Other

Other59th IEEE Global Communications Conference, GLOBECOM 2016
United States
Washington
期間16/12/416/12/8

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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    Bouazizi, M., & Ohtsuki, T. (2017). Sentiment analysis in twitter: From classification to quantification of sentiments within tweets. : 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings [7842262] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2016.7842262