MARKET RESEARCH ANALYSIS OF RETAIL FASHION COSTUMES & APPAREL USING LLM—A QUALITATIVE STUDY

Large language models (LLMs) have a transformative effect on market research analysis with enhanced accuracy and efficiency, particularly in sentiment analysis and trend identification. This study explains how LLMs leverage semisupervised learning and chain-of-thought reasoning to extract valuable insights from unstructured social media data, improving sentiment categorization and predictive analytics with minimal human interaction. This study employs natural language processing (NLP), clustering algorithms, and deep learning architectures for assessing market trends, consumer sentiment, and engagement behaviors. The findings emphasize the effectiveness of LLMs in identifying emerging consumer trends and refining marketing strategies. However, the study also mentions challenges such as bias in terms of generated insights, ethical concerns, and limitations in understanding informal and dynamic digital language. Highlighting the abilities of LLMs in streamlining and simplifying market research processes, this study advances the development of data-driven strategies for companies while urging effective ethical frameworks and high-quality labeled datasets for real-world applications.