Emotion Classification from Text Data using NLP Techniques

This paper presents an approach to emotion classification from textual data using Natural Language Processing (NLP) techniques. The study focuses on preprocessing text, feature extraction, and classification using supervised learning algorithms. The objective is to identify emotional states such as joy, sadness, anger, fear, and surprise from text data. Experimental results show that the proposed model achieves high accuracy and can be applied in sentiment analysis, mental health monitoring, and customer feedback evaluation.