OPINION MINING FOR FEEDBACK MANAGEMENT SYSTEM

Abstract

Opinion mining, also known as sentiment analysis, is a key technique in analyzing textual feedback to determine users’ opinions, emotions, or satisfaction levels. In modern businesses and organizations, large volumes of feedback are collected through surveys, social media, reviews, and customer support channels. Manually analyzing this feedback is time-consuming and prone to errors, making it difficult to derive actionable insights. The proposed Opinion Mining for Feedback Management System leverages Natural Language Processing (NLP) and machine learning techniques to automatically classify feedback as positive, negative, or neutral. The system also identifies key themes and sentiment trends, providing organizations with actionable intelligence to improve services, products, and customer experience. Techniques such as tokenization, stemming, lemmatization, and sentiment scoring are used to preprocess and analyze textual data. By integrating opinion mining into a feedback management system, organizations can efficiently process large amounts of feedback, detect recurring issues, and make informed decisions. This approach enhances decision-making, supports continuous improvement, and ensures that user feedback is effectively utilized to improve organizational performance.