Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method
The advancement of information technology and the widespread use of social media have provided a platform for individuals to express their views on various social issues, including those related to Lesbian, Gay, Bisexual, and Transgender (LGBT) topics. This study aims to assess public sentiment towa...
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LPPM ISB Atma Luhur
2025-07-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2400 |
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| author | Yudhi Franata Rizal Rizki Suwanda |
| author_facet | Yudhi Franata Rizal Rizki Suwanda |
| author_sort | Yudhi Franata |
| collection | DOAJ |
| description | The advancement of information technology and the widespread use of social media have provided a platform for individuals to express their views on various social issues, including those related to Lesbian, Gay, Bisexual, and Transgender (LGBT) topics. This study aims to assess public sentiment towards LGBT issues on Twitter by employing the Naïve Bayes classification algorithm. Relevant tweets were collected through web scraping based on specific LGBT-related keywords within a defined time frame. The collected data underwent several preprocessing stages, including data cleaning, tokenization, stopword removal, and stemming. The processed data were then categorized into three sentiment classes: positive, negative, and neutral. Naïve Bayes was chosen for its effectiveness and efficiency in handling large-scale textual data. The analysis revealed that negative sentiment toward LGBT issues was predominant, although a considerable portion of tweets expressed neutral and positive sentiments. These findings offer valuable insights for policymakers, social activists, and academics in understanding public perception and formulating more effective communication strategies related to LGBT discourse in Indonesia. The classification model achieved an accuracy of 57%, precision of 52%, recall of 100%, and an F1-score of 68%. While the Naïve Bayes approach proved capable in sentiment classification, the model's accuracy could be further enhanced through improved data preparation or the application of more advanced algorithms. |
| format | Article |
| id | doaj-art-532046a296d447bca33e01b89d8b3b10 |
| institution | Matheson Library |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-532046a296d447bca33e01b89d8b3b102025-07-30T02:46:31ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-07-0114329129710.32736/sisfokom.v14i3.24002063Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes MethodYudhi Franata0Rizal1Rizki Suwanda2University of Malikussaleh University of Malikussaleh University of Malikussaleh The advancement of information technology and the widespread use of social media have provided a platform for individuals to express their views on various social issues, including those related to Lesbian, Gay, Bisexual, and Transgender (LGBT) topics. This study aims to assess public sentiment towards LGBT issues on Twitter by employing the Naïve Bayes classification algorithm. Relevant tweets were collected through web scraping based on specific LGBT-related keywords within a defined time frame. The collected data underwent several preprocessing stages, including data cleaning, tokenization, stopword removal, and stemming. The processed data were then categorized into three sentiment classes: positive, negative, and neutral. Naïve Bayes was chosen for its effectiveness and efficiency in handling large-scale textual data. The analysis revealed that negative sentiment toward LGBT issues was predominant, although a considerable portion of tweets expressed neutral and positive sentiments. These findings offer valuable insights for policymakers, social activists, and academics in understanding public perception and formulating more effective communication strategies related to LGBT discourse in Indonesia. The classification model achieved an accuracy of 57%, precision of 52%, recall of 100%, and an F1-score of 68%. While the Naïve Bayes approach proved capable in sentiment classification, the model's accuracy could be further enhanced through improved data preparation or the application of more advanced algorithms.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2400sentiment analysislgbttwitternaïve bayes |
| spellingShingle | Yudhi Franata Rizal Rizki Suwanda Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method Jurnal Sisfokom sentiment analysis lgbt naïve bayes |
| title | Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method |
| title_full | Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method |
| title_fullStr | Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method |
| title_full_unstemmed | Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method |
| title_short | Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method |
| title_sort | analysis of public sentiment towards lgbt on twitter social media using naive bayes method |
| topic | sentiment analysis lgbt naïve bayes |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2400 |
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