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Sentiment Analysis of Youtube Video Comments on Dirty Vote Movie Feri Ferdaus; Wulan Suciska; Anna Gustina Zainal; Eka Yuda Gunawibawa
Jurnal Studi Pemerintahan dan Akuntabilitas Vol 5 No 2 (2026): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jastaka.v5i2.5922

Abstract

Purpose: This study aims to analyze the sentiment of comments on YouTube videos discussing the movie Dirty Vote, which are divided into positive, negative, and neutral categories. Research methodology: The method used is quantitative descriptive, namely content/text analysis of the number of comments and sentiment analysis of comments on the YouTube video content of Dirty Vote uploaded to the Dirty Vote YouTube channel. Results: Results: Based on sentiment analysis of 32,209 comments on the Dirty Vote movie uploaded to YouTube between February 11 and 13, 2024, the sentiment distribution indicates a strong dominance of negative sentiment, with 18,978 comments (59%). This suggests that the movie triggered substantial criticism or strong emotional reactions from viewers. Positive sentiment reached 9,676 comments (30%), reflecting appreciations for the movie’s content, message, or relevance to political discourse. Meanwhile, 3,555 comments (11%) were categorized as neutral, generally consisting of descriptive statements, clarifications, or non-emotive responses. Conclusions: The analysis of 32,209 YouTube comments on Dirty Vote shows dominant negative sentiment (59%), followed by positive (30%) and neutral (11%). This suggests the film strongly impacts audiences, generating both criticism and political awareness, and highlighting how political documentaries can spark debate and polarization. Limitations: References related to sentiment analysis on social media in the perspective of political communication studies are still quite limited and lack depth. Contribution: Enriching the body of research on political communication on social media, particularly YouTube, by using sentiment analysis methods through the BERT model.