南京大学学报（自然科学版）2024，Vol.60Issue(3)：502-510,9.DOI:10.13232/j.cnki.jnju.2024.03.013
基于BiLSTM和CNN的序贯三支情感分类模型研究
Research on sequential three-way sentiment classification model based on BiLSTM and CNN
摘要
Abstract
Text sentiment analysis is an important branch of natural language processing with significant application value.Traditional deep learning models in sentiment analysis mainly perform hard classification based on the size of probability values,neglecting the impact of the data with inconspicuous polarity and resulting in poor accuracy of the classification for threshold edge objects.Based on CNN(Convolutional Neural Networks)and BiLSTM(Bi-directional Long Short-Term Memory),we propose the BiLCNN-S3WD based on BiLSTM and CNN,by introducing the idea of S3WD(Sequential Three-way Decisions),which better processes the data with inconspicuous polarity from multiple granularities.The model's effectiveness is verified through multiple sets of experiments and comparative analyses on the online_shopping_10_cat and Weibo datasets.According to the experimental results,BiLCNN-S3WD achieves better results in each evaluation criterion on the three datasets compared with the seven baseline models.关键词
序贯三支决策/情感分类/CNN/BiLSTM/多粒度Key words
sequential three-way decisions/sentiment classification/CNN/BiLSTM/multi-granularity分类
信息技术与安全科学引用本文复制引用
赵梦宇,孙京博,魏遵天,辛现伟,宋继华..基于BiLSTM和CNN的序贯三支情感分类模型研究[J].南京大学学报（自然科学版）,2024,60(3):502-510,9.基金项目
河南省高等学校重点科研项目(24A520019),2023年国际中文教育研究课题(23YH26C),教育部人文社会科学重点研究基地重大项目(22JJD740017),河南省科技攻关项目(232102210077) （24A520019）
