Sentiment Analysis of User Reviews Integrating Margin Sampling and Tri-training

[Objective] This paper proposes a sentiment analysis method for user reviews integrating margin sampling and tri-training. It addresses the issues of the large volume of user reviews, ambiguous sentiment tendencies, and short content. [Methods] First, we constructed a multi-class support vector machine based on a one-vs-all decomposition strategy. Then, we integrated a margin sampling strategy considering cosine similarity to create an initial set. Finally, we proposed a Tri-training algorithm combining a soft voting mechanism. [Results] The proposed algorithm improved the voting mechanism in the Tri-training algorithm, which further reduced the probability of misjudgment in sample classification by multiple classifiers. All categories achieved precision rates above 79%. [Limitations] The proposed method does not consider extracting information from multimedia data. [Conclusions] Compared with traditional and recently improved semi-supervised learning algorithms, the proposed algorithm demonstrates classification accuracy and efficiency superiority. © 2024 Chinese Academy of Sciences. All rights reserved.