Feedback Sentiment Analayser

Abstract

Sentiment analysis is an essential technique in Natural Language Processing (NLP) that focuses on identifying and categorizing opinions or emotions expressed in text data. This project presents a sentiment analyzer capable of automatically classifying text into positive, negative, and neutral sentiments. The system processes input data using text preprocessing methods such as tokenization, stop-word removal, stemming/lemmatization, and vectorization techniques like TF-IDF. A machine learning model (such as Logistic Regression / Naïve Bayes / LSTM) is trained on labeled datasets to learn sentiment patterns and perform accurate classification. The proposed system helps in understanding user opinions from platforms such as social media, product reviews, and feedback forms. Experimental results show promising accuracy, demonstrating that the sentiment analyzer can be effectively used for real-world opinion mining applications, assisting organizations in decision-making and customer sentiment understanding.