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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LPPM ISB Atma Luhur
2025-07-01
|
| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2400 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2301-7988 2581-0588 |