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Sentimental study of CAA by location-based tweets.

Geetika Vashisht1, Yash Naveen Sinha1

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Summary

This study uses sentiment analysis on Twitter data to gauge public opinion on the Citizenship Amendment Act (CAA). It reveals the national sentiment distribution regarding this significant legislation.

Keywords:
AntiCAAAntiCAA protestsCABCitizen amendment act (CAA)National register of citizen (NRC)Sentiment analysis (SA)Support vector machine (SVM)

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Area of Science:

  • Social Sciences
  • Computational Linguistics
  • Political Science

Background:

  • Twitter serves as a platform for public opinion expression and sentiment dissemination.
  • Sentiment Analysis (SA) is a key technique for understanding mass opinion.
  • The Citizenship Amendment Act (CAA) is a significant and debated piece of legislation.

Purpose of the Study:

  • To analyze public opinion on the Citizenship Amendment Act (CAA) using sentiment analysis.
  • To provide statistical insights into the mass opinion across different Indian states regarding the CAA.
  • To be the first study to apply SA to analyze opinions on the CAA.

Main Methods:

  • Utilizing a machine learning approach for sentiment analysis.
  • Employing a Support Vector Machine (SVM) classifier for tweet classification.
  • Manually annotating and cross-verifying geo-tagged tweets by six annotators.

Main Results:

  • Classification of tweets into positive, negative, and neutral sentiment categories.
  • Detailed statistics on the polarity of mass opinion concerning the CAA across Indian states.
  • Identification of sentiment trends related to the CAA based on Twitter data.

Conclusions:

  • Sentiment analysis provides a viable method for assessing public opinion on contentious political acts like the CAA.
  • The study offers a data-driven perspective on the national sentiment towards the CAA.
  • This research highlights the potential of SA in understanding socio-political discourse.