Improving The Accuracy of Sentiment Analysis using Slang Words Lexicon and Spelling Correction

  • I Komang Surya Adinandika Universitas Udayana
  • I Gusti Agung Gede Arya Kadyanan

Abstract

Text pre-processing has long been a research subject to improve accuracy of Natural Language Processing models. In this paper we propose a technique for text sentiment classification with extra steps on text pre-processing using slang word lexicon and spelling correction to annotate non-formal Indonesian text and normalize them. This study aims to improve the accuracy of sentiment analysis models by strengthening text pre-processing methods. We compared the performance of these preprocessing methods using 2 popular classification algorithms: Support Vector Machine (SVM) and Naïve Bayes, and 3 different feature extraction methods: term presence, Bag of Words, and TF-IDF. Model was trained and tested with 1705 datasets of twitter posts from Indonesian users about Covid 19. Result show

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Published
2023-02-05
How to Cite
ADINANDIKA, I Komang Surya; ARYA KADYANAN, I Gusti Agung Gede. Improving The Accuracy of Sentiment Analysis using Slang Words Lexicon and Spelling Correction. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 12, n. 2, p. 271-276, feb. 2023. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/92534>. Date accessed: 19 nov. 2024. doi: https://doi.org/10.24843/JLK.2023.v12.i02.p04.

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