Chai, Yun Wai (2024) AI for a positive web: Analyzing hate in social media. Final Year Project, UTAR.
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Abstract
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.
| Item Type: | Final Year Project / Dissertation / Thesis (Final Year Project) |
|---|---|
| Subjects: | L Education > L Education (General) T Technology > T Technology (General) T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Faculty of Information and Communication Technology > Bachelor of Information Systems (Honours) Business Information Systems |
| Depositing User: | ML Main Library |
| Date Deposited: | 27 Feb 2025 15:25 |
| Last Modified: | 27 Feb 2025 15:25 |
| URI: | http://eprints.utar.edu.my/id/eprint/7021 |
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