Text-based Emotion Recognition using Machine Learning through Sentiment Analysis

Abstract

This application explores the smooth integration of emotion detection features into a web page. It follows a dual-pronged approach by leveraging natural language processing (NLP) for textual data machine learning models and deep learning models for analyzing the emotion in textual data. Powered by a flask backend, the platform utilizes a natural language toolkit (NLTK) for sentiment analysis, enabling users to put their text for customized emotion detection within an interactive web interface. This hybrid model provides a dynamic environment that not only analyses user emotions but also reacts to them in real time by seamlessly integrating machine learning and deep learning with web development. Its adaptability provides a sophisticated user experience. It extends to a multitude of applications, starting from sentiment-aware recommendation systems to interactive entertainment platforms. The effectiveness of the proposed model is verified by a comprehensive dataset consisting of text labeled with various emotions that are used to train and assess the suggested emotion detection algorithm. Quantitative results demonstrate that the Logistic Regression -based proposed model outperforms competing methods by accurately identifying and classifying emotions in textual data. The best suited model in all the ML algorithms and Deep Learning is Logistic Regression i.e 63%