Emotion Detection from Text Using Machine Learning is a system that identifies and classifies human emotions from textual data. The project uses Natural Language Processing (NLP) techniques to analyze text and detect emotions such as happiness, sadness, anger, fear, surprise, and love. The system first preprocesses the text by removing unnecessary words, symbols, and stop words. It then converts the text into numerical features using techniques like TF-IDF or Bag of Words. Machine learning algorithms are trained on labeled datasets to recognize emotional patterns in text. Once trained, the model can predict the emotion of new text inputs with good accuracy. The system helps in understanding user sentiments and emotional behavior. It can be applied in social media analysis, customer feedback evaluation, mental health monitoring, and chatbot development. Businesses can use it to improve customer satisfaction by analyzing reviews and comments. Healthcare organizations can use it to identify emotional distress in users. The project demonstrates the integration of Machine Learning and NLP to automate emotion recognition from text. It provides valuable insights into human communication and supports better decision-making in various domains.
Poornima et al. (Sun,) studied this question.