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

Mondher Bouazizi, Tomoaki Ohtsuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013289
Publication statusPublished - 2017 Feb 2
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: 2016 Dec 42016 Dec 8


Other59th IEEE Global Communications Conference, GLOBECOM 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

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


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