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.
User ReviewsSentiment AnalysisMargin SamplingTri-Training
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