融合边缘采样和Tri-training的用户评论情感分析方法

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

【目的】针对用户评论数据量大、情感倾向模糊、内容短小等特点,提出融合边缘采样和Tri-training的用户评论情感分析方法。【方法】通过构建基于一对多拆解策略的多分类支持向量机,并融合考虑余弦相似度的边缘采样策略构造初始集,提出结合软投票机制的Tri-training算法。【结果】本文算法对Tri-training算法投票机制的改进,进一步减小了多个分类器对于样本分类投票判断失误的概率,使所有类别精确率均在79%以上。【局限】未考虑多媒体数据的信息提取。【结论】与传统及近年改进的半监督学习算法相比,本文提出的融合边缘采样和Tri-training的算法在分类准确率和效率上具有一定的优越性。

[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.