GENAI-POWERED SENTIMENT ANALYSIS FOR MARKET RESEARCH

Abstract

Generative Artificial Intelligence powered Sentiment Analysis for Market Research is an advanced application of Natural Language Processing that helps businesses analyze customer opinions, emotions, and feedback from textual data sources such as product reviews, social media posts, surveys, and online comments. In the modern digital world, organizations generate and collect massive amounts of textual information every day, making it essential to understand customer sentiment for effective business decision-making and market analysis. Traditional sentiment analysis methods mainly rely on rule-based systems or basic machine learning algorithms, which often struggle to understand contextual meaning, sarcasm, tone, and complex sentence structures. These limitations reduce the accuracy and reliability of sentiment classification. To overcome these challenges, the proposed system utilizes Generative AI models based on Transformer architectures and Large Language Models (LLMs), which can analyze language context and semantics more effectively. The proposed system performs several preprocessing operations such as text cleaning, tokenization, stop-word removal, and normalization to improve data quality before analysis. The processed textual data is then passed to a pre-trained AI model that classifies the sentiment into categories such as positive, negative, or neutral. The system can also identify trends, customer preferences, and emotional patterns from large datasets. The analyzed results are presented in a structured and understandable format to support business intelligence and strategic planning.