基于特征融合的中文文本情感分析方法

Sentiment analysis of Chinese text based on feature fusion

针对现有的中文文本情感分析方法不能从句法结构、上下文信息和局部语义特征等方面综合考量文本语义信息的问题,提出一种基于特征融合的中文文本情感分析方法.首先,采用Jieba分词工具对评论文本进行分词和词性标注,并采用词向量训练工具GloVe获取融入词性的预训练词向量;然后,将词向量分别作为引入Self-Attention的BiGRU和TextCNN的输入,使用引入Self-Attention的BiGRU从文本的句法结构和文本的上下文信息两个方面综合提取全局特征,使用TextCNN提取文本的局部语义特征;最后,将全局特征和局部语义特征进行融合,并使用Softmax进行文本情感分类.实验结果表明,本文方法可以有效提高文本情感分析的准确率.

To address the problems that the existing methods cannot comprehensively consider the semantic information of text in terms of syntactic structure,contextual information and local semantic features,a feature fusion-based Chinese text sentiment analysis method is proposed.Firstly,Jieba word segmentation tool is used for word segmentation and part of speech tagging of the review text,and a word vector training tool GloVe is used to obtain pre-trained word vectors with part of speech.Then,the word vector is used as the input of BiGRU and TextCNN with self attention respectively.The global features are extracted from the syntactic structure and the contextual information of the text using BiGRU with Self-Attention,and the local semantic ones are extracted using TextCNN.Finally,the global features and local semantic features are integrated,and Softmax is used for text sentiment classification.The experiment result shows that the proposed method can effectively improve the accuracy of text sentiment analysis.