Survey Paper on Retail Item Sales Analysis and Item Recommendations Scoring

Understanding consumer sentiment has become essential to improving the buying experience as e-commerce and internet platforms have grown rapidly. In order to recommend stores and items based on user feedback, this survey study investigates the use of machine learning approaches in customer sentiment analysis. It examines several techniques used for sentiment recognition, such as deep learning models and natural language processing (NLP), and assesses how well they handle a variety of unstructured data. Modern algorithms for text classification, opinion mining, and recommendation systems are highlighted in the study along with a comparison of their uses and performance. It also looks at issues including data sparsity, sarcasm recognition, and linguistic variety, and suggests future paths for the area. In order to provide insights for researchers and practitioners looking to enhance user pleasure and business outcomes, this work attempts to bridge the gap between customer sentiment analysis and intelligent recommendation systems by synthesizing the most recent developments