The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis
Campaigns significantly influence candidate electability. Presidential debates, a key campaign strategy, generate extensive public comments on social media, reflecting voter sentiment. This study employs VADER for automated sentiment labeling and Naïve Bayes for classification, analyzing comments fr...
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| Format: | Article |
| Language: | English |
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Informatics Department, Faculty of Computer Science Bina Darma University
2025-03-01
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| Series: | Journal of Information Systems and Informatics |
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| Online Access: | https://journal-isi.org/index.php/isi/article/view/1001 |
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| author | Arnoldus Yitzhak Petra Manoppo Wirawan Istiono |
| author_facet | Arnoldus Yitzhak Petra Manoppo Wirawan Istiono |
| author_sort | Arnoldus Yitzhak Petra Manoppo |
| collection | DOAJ |
| description | Campaigns significantly influence candidate electability. Presidential debates, a key campaign strategy, generate extensive public comments on social media, reflecting voter sentiment. This study employs VADER for automated sentiment labeling and Naïve Bayes for classification, analyzing comments from the KPU and Najwa Shihab YouTube channels. Electability data were sourced from national survey reports for correlation analysis. Pearson correlation results indicate that positive sentiment has a moderate negative correlation with electability, while negative sentiment shows a strong positive correlation. This suggests that negative sentiment in YouTube comments is more indicative of a candidate’s rising electability, whereas positive sentiment does not necessarily translate into increased support. The Naïve Bayes model achieved 65% accuracy, 59% precision, 57% recall, and 57% F1-score when including neutral comments. Excluding neutral comments improved accuracy to 77%, with 68% precision, 68% recall, and 67% F1-score. The dataset comprised 17,872 comments, ensuring a robust sample. Despite these findings, limitations exist, such as potential biases in sentiment classification and representativeness, as social media users may not fully reflect the general voting population. Furthermore, while correlation is established, causality remains uncertain, requiring further research. This study enhances the understanding of social media sentiment in political campaigns and highlights the importance of integrating online sentiment analysis with traditional polling methods for a comprehensive assessment of electability. |
| format | Article |
| id | doaj-art-c0a2be7bb65d4ab5a1c1a0517460b4c0 |
| institution | DOAJ |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-c0a2be7bb65d4ab5a1c1a0517460b4c02025-08-20T02:55:09ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822025-03-017175877810.51519/journalisi.v7i1.10011001The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson AnalysisArnoldus Yitzhak Petra Manoppo0Wirawan Istiono1Universitas Multimedia NusantaraUniversitas Multimedia NusantaraCampaigns significantly influence candidate electability. Presidential debates, a key campaign strategy, generate extensive public comments on social media, reflecting voter sentiment. This study employs VADER for automated sentiment labeling and Naïve Bayes for classification, analyzing comments from the KPU and Najwa Shihab YouTube channels. Electability data were sourced from national survey reports for correlation analysis. Pearson correlation results indicate that positive sentiment has a moderate negative correlation with electability, while negative sentiment shows a strong positive correlation. This suggests that negative sentiment in YouTube comments is more indicative of a candidate’s rising electability, whereas positive sentiment does not necessarily translate into increased support. The Naïve Bayes model achieved 65% accuracy, 59% precision, 57% recall, and 57% F1-score when including neutral comments. Excluding neutral comments improved accuracy to 77%, with 68% precision, 68% recall, and 67% F1-score. The dataset comprised 17,872 comments, ensuring a robust sample. Despite these findings, limitations exist, such as potential biases in sentiment classification and representativeness, as social media users may not fully reflect the general voting population. Furthermore, while correlation is established, causality remains uncertain, requiring further research. This study enhances the understanding of social media sentiment in political campaigns and highlights the importance of integrating online sentiment analysis with traditional polling methods for a comprehensive assessment of electability.https://journal-isi.org/index.php/isi/article/view/1001machine learningnaïve bayespearson correlationpresidential debatesentiment analysis |
| spellingShingle | Arnoldus Yitzhak Petra Manoppo Wirawan Istiono The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis Journal of Information Systems and Informatics machine learning naïve bayes pearson correlation presidential debate sentiment analysis |
| title | The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis |
| title_full | The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis |
| title_fullStr | The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis |
| title_full_unstemmed | The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis |
| title_short | The Influence of Presidential Debate Comment Sentiment on YouTube on Candidate Electability: Naïve Bayes and Pearson Analysis |
| title_sort | influence of presidential debate comment sentiment on youtube on candidate electability naive bayes and pearson analysis |
| topic | machine learning naïve bayes pearson correlation presidential debate sentiment analysis |
| url | https://journal-isi.org/index.php/isi/article/view/1001 |
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