Sentiment analysis of sales transactions for customer behavior and revenue prediction

The development of sentiment analysis and sales transaction analytics is a major breakthrough in customer intelligence and sales prediction using data. Although the conventional revenue prediction models depend on structured transactional variables, where price, quantity, frequency and promotional activity play the key role, emerging knowledge remains that the unstructured customer generated text offers complementary behavioral variables that improve the predictive outcome. Recent developments in deep learning, multimodal learning models, and interpretable machine learning have made it possible to systematically incorporate the synthesized and unstructured data streams into coherent predictive models. This review has looked at methodological advances, modelling approaches and experimental results on sentiment-enhanced customer behavior modelling and revenue prediction. The discussion has identified the contribution of sentiment polarity, emotional intensity, and contextual embeddings to the churn prediction, repeat purchase probability and multi-horizon revenue forecasting. It was found that the main issues were to integrate the data, provide interpretability of models, reduce bias, achieve scalability, and cross-domain generalization. Another key point made in the review is that explainable AI frameworks are necessary in order to provide managerial trust and regulatory adherence in high-stakes decisions. The results suggest that sentiment enhanced forecasting models are incremental predictors when they are appropriately calibrated and tested. Nevertheless, the next generation of studies should concern the standardization of methods, the multimodal alignment, and the ethical issues to allow the application of such devices in commercial systems.