基于混合向量模型的中文评论情感分析

Sentiment Analysis of Chinese Comments Based on Hybrid Vector Model

为解决TF-IDF模型表达情感信息不足的问题,提出一种情感Senti模型,通过该模型提取文本中的情感信息,包括句子中积极/消极的情感词、否定词、转折词以及程度副词,考虑标点符号在句子中起到的情感作用,利用情感词典和语义规则提取情感信息,生成相应的情感矩阵。在此基础上,与TF-IDF模型进行拼接,形成混合向量模型。实验结果表明,与只运用TF-IDF模型相比,混合向量模型精确度更高,具有较好的分类效果。

In order to solve the problem of insufficient expression of sentiment information in the TF-IDF model,this paper proposes the Senti model to extract the sentiment information in the text,including positive/negative sentiment words,negative words,transition words and adverbs of degree in the sentences.The sentiment function of punctuations in the sentences is considered herein,and the sentiment dictionary and semantic rules are used to extract sentiment information,thus generating the corresponding sentiment matrix.On this basis,the proposed model is spliced with the TF-IDF model to form a hybrid vector model.Experimental results show that compared with the TF-IDF alone,the hybrid vector model shows higher accuracy and better classification effect.