Research Progress of Review Topic Mining Methods: From Word Frequency Statistics to Deep Learning

Review topic mining involves extracting specific evaluation themes from user-generated reviews by identifying characteristic words, serving as the basis for fine-grained opinion analysis and sentiment recognition within review texts. The rapid identification of latent topics from vast collections of review data has remained a prominent and persistent concern within the field of natural language processing. This paper provides a comprehensive review of research progress in review topic mining, introducing the fundamental concepts of review topic mining. Three distinct approaches to review topic mining are discussed, including review topic mining based on word frequency statistics, review topic mining using topic models, and review topic mining that integrates deep learning models. Particular emphasis is placed on exploring cutting-edge methods within these review topic mining approaches. Finally, an analysis of the development trends of deep learning in the field of natural language processing is presented.