Opinion mining in Twitter: How to make use of sarcasm to enhance sentiment analysis

Mondher Bouazizi, Tomoaki Ohtsuki

研究成果: Conference contribution

49 被引用数 (Scopus)

抄録

Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.

本文言語English
ホスト出版物のタイトルProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
出版社Association for Computing Machinery, Inc
ページ1594-1597
ページ数4
ISBN(印刷版)9781450338547
DOI
出版ステータスPublished - 2015 8月 25
イベントIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
継続期間: 2015 8月 252015 8月 28

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
国/地域France
CityParis
Period15/8/2515/8/28

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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