Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote'

Authors

  • Aisha Shakila Iedwan Universitas Amikom Yogyakarta Author
  • Nia Mauliza Universitas Amikom Yogyakarta Author
  • Yoga Pristyanto Universitas Amikom Yogyakarta Author
  • Anggit Dwi Hartanto Universitas Amikom Yogyakarta Author
  • Arif Nur Rohman Universitas Amikom Yogyakarta Author

DOI:

https://doi.org/10.15294/sji.v11i3.10290

Keywords:

Sentiment analysis, Dirty vote, SVM, Multinominal Naïve Bayes

Abstract

Purpose: The purpose of this research is to analyze and compare the performance of two machine learning models, Support Vector Machine (SVM) and Multinomial Naive Bayes, in conducting sentiment analysis on YouTube comments related to the film "Dirty Vote."

Methods: The study involved collecting YouTube comments and preprocessing the data through cleaning, labeling, and feature extraction using TF-IDF. The dataset was then divided into training and testing sets in an 80:20 ratio. Both the SVM and Multinomial Naive Bayes models were trained and tested, with their performance evaluated using accuracy, precision, recall, and F1-score metrics.

Result: The results revealed that both models performed well in classifying sentiments, with SVM slightly outperforming Multinomial Naive Bayes in terms of accuracy and precision. Particularly, SVM showed superior performance in detecting positive comments, making it a more reliable model for this specific sentiment analysis task.

Novelty: This study contributes to the field of sentiment analysis by providing a detailed comparative analysis of SVM and Multinomial Naive Bayes models on YouTube comments in the context of an Indonesian film. The findings highlight the strengths and weaknesses of each model, offering insights into their applicability for sentiment analysis tasks, particularly in analyzing social media content. This research also suggests potential future directions, including the exploration of advanced NLP techniques and different models to enhance sentiment analysis performance.

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Article ID

10290

Published

05-11-2024

Issue

Section

Articles

How to Cite

Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film ’Dirty Vote’. (2024). Scientific Journal of Informatics, 11(3), 839-848. https://doi.org/10.15294/sji.v11i3.10290