A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter

Mondher Bouazizi, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review

123 Citations (Scopus)


Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented toward the binary classification (i.e., classification into 'positive' and 'negative') or the ternary classification (i.e., classification into 'positive,' 'negative,' and 'neutral') of texts. In this paper, we propose a novel approach that, in addition to the aforementioned tasks of binary and ternary classifications, goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes. While in this paper, we limit our scope to seven different sentiment classes, the proposed approach is scalable and can be run to classify texts into more classes. We first introduce SENTA, our tool built to help users select out of a wide variety of features the ones that fit the most for their application, to run the classification, through an easy-to-use graphical user interface. We then use SENTA to run our own experiments of multi-class classification. Our experiments show that the proposed approach can reach up to 60.2% accuracy on the multi-class classification. Nevertheless, the approach proves to be very accurate in binary classification and ternary classification: In the former case, we reach an accuracy of 81.3% for the same data set used after removing neutral tweets, and in the latter case, we reached an accuracy of classification equal to 70.1%.

Original languageEnglish
Article number8013027
Pages (from-to)20617-20639
Number of pages23
JournalIEEE Access
Publication statusPublished - 2017 Aug 18


  • Twitter
  • machine learning
  • sentiment analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter'. Together they form a unique fingerprint.

Cite this