Predicting product sales performance using various types of customer review data

Today, in the e-commerce world, product reviews are a critical part of influencing consumer buying decisions and offer valuable insight to determine sales quality. But many current methods do not make efficient use of heterogeneous user-generated content (UGC) and those they predict with a unified model may ignore the different nature between various review types. In light of these limitations, this study introduces an integrated algorithmic framework that combines cutting-edge sentiment analyses and machine learning (ML) algorithms for sales quality prediction through automatic analysis of product reviews over the internet. The approach proposed will collect structured data from different sources during a systematic process and then consider the path of normalization, and sentiment analysis followed by feature selection to construct advanced prognosis models. The model proved highly effective, achieving an 88% accuracy rate in predicting sales quality. This strong performance indicates a significant correlation between sales performance and sentiment reviews. This new framework shows good promise that sentiment analysis in UGC can be used and deployed in e-commerce product evaluation and recommendation systems. Further research should investigate the integration of regional and temporal dynamics to improve model accuracy.