The main aim of this project is to develop an intelligent system that can automatically detect inappropriate and abusive language from comments posted on social media platforms. The goal is to protect users from online harassment, cyberbullying, and verbal abuse by preventing the display of such harmful content. This is particularly valuable in student discussion platforms where learning environments must be positive and inclusive. Since language evolves constantly, and abusive words may vary with time and context, the project will also explore strategies to handle out-of-vocabulary (OOV) words by using character-level or sub-word embeddings. This ensures the model can generalize well to detect new or previously unseen abusive language. The expected outcome is a practical, deployable solution that contributes to a safer and more respectful digital communication environment.
To complete this project successfully, students will begin by understanding the foundations of NLP and transfer learning, particularly BERT. They will collect or prepare datasets containing social media comments, label them appropriately, and preprocess the data for model training. Key tasks include importing necessary libraries, implementing text classification models, fine-tuning BERT for abusive comment detection, and handling challenges such as ambiguous or context-dependent language. Students will also build a system capable of real-time or batch processing of user input, test the model for performance and accuracy, and iteratively refine it to improve detection results.
Other important activities include writing clean and ethical code, documenting the methodology and outcomes, and delivering a final presentation. Infrastructure-wise, students will use Python (via Anaconda or Google Colab), install required ML libraries, and ensure their development environment is capable of handling NLP models. Proper coding ethics and adherence to plagiarism-free development will be strictly enforced.
