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

Iedwan, Aisha Shakila; Mauliza, Nia; Pristyanto, Yoga; Hartanto, Anggit Dwi; Rohman, Arif Nur

doi:10.15294/sji.v11i3.10290

Journal

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

Scientific Journal of Informatics · Vol. 11 No. 3 (2024) · pp. 839–848

DOI: 10.15294/sji.v11i3.10290
ISSN: 2407-7658, 2460-0040
Published: 2024-11-05
Publisher: Universitas Negeri Semarang
Language: eng

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.

Keywords

Collaboration network of the authors

25 authors · 58 co-authorship links · 4 across different organisations

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Universitas Amikom Yogyakarta
Politeknik Perkapalan Negeri Surabaya
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Ema Utami	Universitas Amikom Yogyakarta	4
Ahmad Fikri Iskandar	Universitas Amikom Yogyakarta	2
Erwin Syahrudin	Universitas Amikom Yogyakarta	2
Wahyu Hidayat	Universitas Amikom Yogyakarta	1
Agung Budi Prasetyo	Politeknik Perkapalan Negeri Surabaya	1
Akhmad Dahlan	Universitas Amikom Yogyakarta	1
Hanif Al Fatta	Affiliation unknown	1
Lilis Dwi Farida	Universitas Amikom Yogyakarta	1
Hastari Utama	Universitas Amikom Yogyakarta	1
Wahyu Krisna Hidayat	Universitas Amikom Yogyakarta	1
Agung Prasetio Budi	Universitas Amikom Yogyakarta	1
Sumarni Adi	Universitas Amikom Yogyakarta	1
Mardhiya Hayaty	Universitas Amikom Yogyakarta	1
Bety Wulan Sari	Universitas Amikom Yogyakarta	1
Donni Prabowo	Universitas Amikom Yogyakarta	1
Afrig Aminuddin	Universitas Amikom Yogyakarta	1
Irma Rofni Wulandari	Affiliation unknown	1
Ibrahim Aji Fajar Romadhon	Affiliation unknown	1
Anggit Ferdita Nugraha	Affiliation unknown	1
Atik Nurmasani	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote'</dc:title>
	<dc:creator xml:lang="en">Iedwan, Aisha Shakila</dc:creator>
	<dc:creator xml:lang="en">Mauliza, Nia</dc:creator>
	<dc:creator xml:lang="en">Pristyanto, Yoga</dc:creator>
	<dc:creator xml:lang="en">Hartanto, Anggit Dwi</dc:creator>
	<dc:creator xml:lang="en">Rohman, Arif Nur</dc:creator>
	<dc:subject xml:lang="en">Sentiment analysis</dc:subject>
	<dc:subject xml:lang="en">Dirty vote</dc:subject>
	<dc:subject xml:lang="en">SVM</dc:subject>
	<dc:subject xml:lang="en">Multinominal Naïve Bayes</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-11-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/10290</dc:identifier>
	<dc:identifier>10.15294/sji.v11i3.10290</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 3: Special Issue; 839-848</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v11i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/10290/1975</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
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