AI for a positive web: Analyzing hate in social media
Hate speech detection on social media is a significant challenge due to the diverse and evolving nature of online language. This project aims to create an effective and user-friendly hate speech detection system using advanced machine learning and deep learning techniques. By developing various mode...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/7021/ http://eprints.utar.edu.my/7021/1/fyp_IB_2024_CYW.pdf |
| _version_ | 1848886827613683712 |
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| author | Chai, Yun Wai |
| author_facet | Chai, Yun Wai |
| author_sort | Chai, Yun Wai |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Hate speech detection on social media is a significant challenge due to the diverse and evolving nature of online language. This project aims to create an effective and user-friendly hate speech detection system using advanced machine learning and deep learning techniques. By developing various models, including Logistic Regression, Naive Bayes, Decision Trees, LSTM, BiLSTM, and CNN-LSTM, and incorporating an ensemble learning approach with a voting classifier, the system improves detection accuracy and reliability. A web interface built with Streamlit allows users to test text inputs and understand model decisions through explainability tools like SHAP and LIME. The best model achieved an accuracy of 88% with strong precision and recall, demonstrating the effectiveness of the proposed solution in detecting hate speech while mainta CNN_LSTM Training Phase ining interpretability and ease of use. |
| first_indexed | 2025-11-15T19:44:41Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-7021 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:41Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-70212025-02-27T07:25:16Z AI for a positive web: Analyzing hate in social media Chai, Yun Wai L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering Hate speech detection on social media is a significant challenge due to the diverse and evolving nature of online language. This project aims to create an effective and user-friendly hate speech detection system using advanced machine learning and deep learning techniques. By developing various models, including Logistic Regression, Naive Bayes, Decision Trees, LSTM, BiLSTM, and CNN-LSTM, and incorporating an ensemble learning approach with a voting classifier, the system improves detection accuracy and reliability. A web interface built with Streamlit allows users to test text inputs and understand model decisions through explainability tools like SHAP and LIME. The best model achieved an accuracy of 88% with strong precision and recall, demonstrating the effectiveness of the proposed solution in detecting hate speech while mainta CNN_LSTM Training Phase ining interpretability and ease of use. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7021/1/fyp_IB_2024_CYW.pdf Chai, Yun Wai (2024) AI for a positive web: Analyzing hate in social media. Final Year Project, UTAR. http://eprints.utar.edu.my/7021/ |
| spellingShingle | L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering Chai, Yun Wai AI for a positive web: Analyzing hate in social media |
| title | AI for a positive web: Analyzing hate in social media |
| title_full | AI for a positive web: Analyzing hate in social media |
| title_fullStr | AI for a positive web: Analyzing hate in social media |
| title_full_unstemmed | AI for a positive web: Analyzing hate in social media |
| title_short | AI for a positive web: Analyzing hate in social media |
| title_sort | ai for a positive web: analyzing hate in social media |
| topic | L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/7021/ http://eprints.utar.edu.my/7021/1/fyp_IB_2024_CYW.pdf |