The project, "Enhancing Aspect-based Sentiment Analysis with Masked Language Modeling for Affective Token Prediction," aims to advance the field of sentiment analysis by focusing on aspect-level sentiment detection using Twitter data. The system integrates state-of-the-art machine learning models, including BERT and RoBERTa for masked language modeling and contextual understanding, and sequential models like LSTM and GRU for capturing long-term dependencies in text. By employing these algorithms, the system performs fine-grained analysis to predict sentiments (positive, negative, or neutral) for each aspect within the provided input text. This hybrid approach ensures higher accuracy and scalability, enabling applications in various fields such as customer feedback analysis, market research, and opinion mining. The frontend, built using HTML, CSS, and JavaScript, ensures a user-friendly interface, while the backend, powered by Python, Django, provides efficient data management and model deployment. This project underscores the importance of combining contextual and sequential models for improved sentiment analysis performance.

Keywords
Sentiment analysis, Aspect-level sentiment, Masked language modeling, BERT, RoBERTa, LSTM, GRU, Twitter data analysis, Django application, Affective token prediction.