基于多约简Fisher-VSM和SVM的文本情感分类

TEXT SENTIMENT CLASSIFICATION BASED ON MULTI-REDUCED FISHER-VSM AND SVM

为了提高文本情感分类准确率,提出基于多约简Fisher向量空间模型和支持向量机的文本情感分类算法。该算法首先采用Fisher判别准则提取TF-IDF特征向量,然后依据低维文档向量空间模型间的相似度对文档进行聚类,减少文档的数目。该算法从维度和数量两个方面对文档的向量空间模型进行约简,以期提高支持向量机的训练速度和分类性能。仿真实验结果表明,该算法具有良好的召回率和分类准确率。

We propose a novel text sentiment classification algorithm in this paper, it is based on multi-reduced Fisher-VSM and SYM, to improve tile accuracy of text sentiment classification. The algorithm first adopts Fisher＇ s discriminant criterion to extract TF-IDF eigenvector, and then clusters the documents according to the similarity between vector space models of low-dimension documents so as to reduce their numbers. The algorithm makes reduction on vector space model of documents from two aspects of dimensionality and number so as to improve the training speed and classification performance of SVM. Simulation experimental results demonstrate that the proposed algorithm has good re- call ratio and classification accuracy.