GENAI-POWERED SENTIMENT ANALYSIS FORMARKET RESEARCH

Abstract

Sentiment Analysis, also known as opinion mining, is an important application of Natural Language Processing that focuses on identifying and classifying opinions, emotions, and attitudes expressed in textual data. This project presents a Generative Artificial Intelligence powered sentiment analysis system developed specifically for market research and product review analysis. The primary objective of the system is to automatically classify customer feedback into positive, negative, and neutral categories, helping businesses understand customer satisfaction levels and improve products and services effectively. The rapid expansion of e-commerce platforms and digital communication has resulted in a massive increase in user-generated content such as product reviews, ratings, comments, and social media discussions. Manually analyzing such large-scale textual data is time-consuming, inefficient, and prone to human error. Therefore, there is a strong demand for intelligent automated systems capable of processing and analyzing customer opinions accurately and efficiently. This project addresses that challenge by leveraging advanced AI techniques including machine learning, deep learning, and Transformer-based language models. The proposed system performs multiple preprocessing operations such as text cleaning, normalization, tokenization, and feature extraction to prepare raw textual data for analysis. Advanced Generative AI models such as BERT and GPT are utilized because of their ability to understand semantic meaning, contextual relationships, emotional tone, and complex sentence structures in text. These Transformer-based models significantly improve sentiment classification accuracy compared to traditional rule-based and machine learning approaches. The system is evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score to measure analytical effectiveness and classification quality.