融合语法规则的Bi-LSTM中文情感分类方法研究

Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules

【目的】提出一种融合语法规则的情感分类方法,提高中文文本情感分类的准确率。【方法】将中文语法规则以约束的形式同Bi-LSTM结合,通过规范句子相邻位置的输出模拟句子层次中非情感词、情感词、否定词和程度词的语言作用。【结果】相较于前沿的RNN、LSTM、Bi-LSTM模型,融合中文语法规则的Bi-LSTM模型准确率可达91.2%,在准确率方面得到较好的提升。【局限】实验数据集来源相对单一,只选取酒店评论数据集,在其他数据集上方法的有效性需要进一步验证。【结论】本文提出的情感分类方法融合了中文语法规则,进一步提升了情感分类的准确率。

[Objective]This paper proposes a new classification method based on grammar rules,aiming to improve the accuracy of sentiment analysis for Chinese texts.[Methods]Firstly,we combined the Chinese grammar rules with Bi-LSTM in the form of constraints and standardized the adjacent positions of sentences from the experimental corpus.Then,we generated the linguistic functions of non-emotional,emotional,negative,and degree words at sentence level.[Results]Compared with the RNN,LSTM and Bi-LSTM models,the accuracy of our model reached upto 91.2%.[Limitations]The experimental data was only collected from the hotel reviews.More research is needed to examine the performance of this model on other data sets.[Conclusions]The proposed method improves the accuracy of sentiment classification for Chinese.