BERT-Powered Multilingual Sentiment Analysis with Emotion Detection

This paper involves developing efficient method for emotion-driven sentiment analysis across multiple languages. It uses a BERT-based model for emotion detection, along with language detection and translation modules, to analyze user text or reviews. The application can handle negations and provides visual feedback through emotion-to-image mapping, enhancing user experience. It supports both single and bulk review analysis, making it useful for individuals and businesses. By integrating NLP techniques with web development, the project demonstrates the potential of multilingual sentiment analysis to improve user experience and decision-making in diverse linguistic contexts. This highlights the importance of combining advanced NLP models with practical applications to address real-world challenges, enabling businesses and individuals to better understand and respond to global user sentiments.