河北工业科技2024，Vol.41Issue(2)：92-98,7.DOI:10.7535/hbgykj.2024yx02002
基于结构-语义融合的评论文本情感分类研究
A study on sentiment classification of commentary text based on structural-semantic fusion
摘要
Abstract
To solve the problem that some current sentiment analysis models unilaterally rely on textual semantic features and neglect syntactic structural features,a sentiment classification method based on structural-semantic fusion was proposed.The method fused syntactic structural information with semantic information to comprehensively extract text features.The BERT(Bidirectional Encoder Representation from Transformers)model was introduced into the TreeLSTM(Tree-structured bidirectional LSTM)network structure.Then the data enhancement of the vector representation obtained by BERT was performed by utilizing the SimCSE(Simple Contrastive Learning of Sentence Embeddings)model's self-supervised training.Finally the structural semantic feature fusion was realized on the constructed TreeLSTM network by means of node encoding,and was analyzed in a multi-group comparison with the baseline model.The experimental results show that on the SST(Stanford Sentiment Tree-bank)dataset released by Stanford University,the structural-semantic fusion-based sentiment classification method obtains higher accuracy compared to the classical tree-structured sentiment classification model,with an accuracy rate of 96.79%in the binary classification task.The proposed method can comprehensively and effectively extract the features of the comment text and enhance the vector representation of the text,which is important for text processing in the field of natural language processing.关键词
自然语言处理/情感分类/语义融合/预训练模型/句法结构Key words
natural language processing/sentiment classification/semantic fusion/pre-trained model/syntactic structure分类
信息技术与安全科学引用本文复制引用
马艳珍,勾智楠,池云仙,高凯..基于结构-语义融合的评论文本情感分类研究[J].河北工业科技,2024,41(2):92-98,7.基金项目
2024年度人文社会科学研究重大课题攻关项目(ZD202402) （ZD202402）
