The main goal of the project is to develop a robust and reliable hate speech detection system that can be integrated with social media platforms to identify and block harmful or offensive content in real-time. The project addresses the growing concern around online harassment, bullying, and hate speech, which affects user experience and societal harmony. Students working on this project will use a labeled dataset of hate and non-hate speech to train a machine learning model that can differentiate between normal and harmful language. Word embeddings and contextual understanding will be used to enhance semantic accuracy. Ultimately, the project aims to equip students with a deep understanding of NLP, machine learning, and ethical AI applications while solving a real-world social issue.
To successfully complete the project, students will follow a structured twelve-week timeline. Initially, they will install Python and set up a development environment using tools like Anaconda Navigator or Google Colab. They will begin by learning the backbone of the model and exploring relevant ML libraries. Students will then build a basic framework, collect or use existing hate speech datasets, and preprocess the data.
Following this, students will train the model using various machine learning algorithms and test its performance with new data. Over time, they will improve the model’s accuracy through tuning and validation. Final weeks are allocated to refining the model, documenting the entire process, and preparing for a team presentation. The project also includes exploring the use of GPT or similar NLP models, integrating word embeddings for context analysis, and maintaining strict coding ethics and documentation practices.
