Deep Learning-based Sentiment Classification and Mining for Online Reviews

In the digital age, online reviews have become an important source of information for the restaurant industry to obtain service feedback. The classification and analysis of online comments are of great significance for catering enterprises to understand customer needs and improve product and service quality. This paper aims to explore and uncover the underlying reasons for customers giving different emotional reviews by analyzing the emotional tendencies in their feedback, and subsequently, develop an emotional analysis model. The research findings reveal that consumers primarily focus on packaging quality, service attitude, delivery speed, and taste. By utilizing a logistic regression model based on word2vec, it is possible to effectively differentiate the emotional tendencies of various food reviews for different catering enterprises.