Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting

Authors

  • Salsabila Rahadatul Aisy Universitas Negeri Semarang Author
  • Budi Prasetiyo Universitas Negeri Semarang Author

DOI:

https://doi.org/10.15294/zbxqcc36

Keywords:

sentiment analysis, TPKS Law, multinomial naive Bayes, support vector machine, soft voting, InSet lexicon

Abstract

Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law.

Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law.

Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm.

Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law.

Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.

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Published

2023-09-29

Article ID

35228

How to Cite

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting. (2023). Recursive Journal of Informatics, 1(2), 93-101. https://doi.org/10.15294/zbxqcc36