Leveraging semantic features for recommendation: Sentence-level emotion analysis

Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid person-alized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods. A novel voting mechanism is also designed based on positive preferences from the neighbors of the target user to directly generate the recommen-dation results. The experimental results of testing the proposed model with a real-world data set show that our proposed model can achieve better recommendation effects than the representative recommendation algorithms. In addition, we demonstrated that fine-grained emotion recognition has good adaptability to a sparse rating matrix with a reasonable and good performance.