Emotion Detection using Natural Language Processing and Streamlit

Emotion recognition from textual data is a key Natural Language Processing (NLP) area with extensive applications in mental health, customer care, and human-computer interaction. This research paper delivers the design and development of an Emotion Detector Web Application with Python, NLP practices, and Streamlit. The project aims to categorize human emotions from text into six categories: Happy, Sad, Angry, Fear, Surprise, and Neutral. The system being proposed makes use of text pre-processing, tokenization, and a pre-trained machine learning model in order to analyze the input and identify emotional tone. The interface is easy to use and provides an interactive real-time experience. The paper also includes results from different user inputs and mentions the practical applications and future improvements of the system.