Utilizing hashtags for sentiment analysis of tweets in the political domain

Ika Alfina, Dinda Sigmawaty, Fitriasari Nurhidayati, Achmad Nizar Hidayanto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Citations (Scopus)

Abstract

The objective of this research is to investigate the benefit of utilizing hashtags to determine sentiment polarity of tweets in the political domain. We used the sentiment polarity of hashtags as the features in classification, proposed rules for automatically annotating dataset based on the number of positive and negative hashtags in the tweets, and proposed a method to enrich terms in the tweet by extracting hashtag terms. We named the number of positive and negative hashtags as SentiHT feature. The experiments and evaluation show that sentiment classification using SentiHT feature and the automatically labeled dataset using SentiHT has a very good accuracy of more than 95%. Moreover, SentiHT outperforms unigram feature when combined with Naïve Bayes, SVM or Logistic Regression algorithms, but the opposite occurs when using Random Forest algorithm. Based on computing time to build the model, we recommend using SentiHT feature combined with Naïve Bayes algorithm.

Original languageEnglish
Title of host publicationProceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
PublisherAssociation for Computing Machinery
Pages43-47
Number of pages5
ISBN (Electronic)9781450348171
DOIs
Publication statusPublished - 24 Feb 2017
Event9th International Conference on Machine Learning and Computing, ICMLC 2017 - Singapore, Singapore
Duration: 24 Feb 201726 Feb 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F128357

Conference

Conference9th International Conference on Machine Learning and Computing, ICMLC 2017
CountrySingapore
CitySingapore
Period24/02/1726/02/17

Keywords

  • Machine learning
  • NLP
  • Politics
  • Sentiment analysis
  • Twitter

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