文献类型：期刊文献

中文题名：融合个体偏差信息的文本情感分析模型

英文题名：Text sentiment analysis model based on individual bias information

作者：陈丽安[1];过弋[1,2,3]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]大数据流通与交易技术国家工程实验室(上海数据交易所),上海200436;[3]上海大数据与互联网受众工程技术研究中心,上海200072

年份：2024

卷号：44

期号：1

起止页码：145

中文期刊名：计算机应用

外文期刊名：journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2023】；CSCD:【CSCD_E2023_2024】；

基金：上海市科学技术委员会科技计划项目(22DZ1204903,22511104800)。

语种：中文

中文关键词：文本情感分析;自注意力机制;交叉注意力机制;Transformer模型;长短期记忆网络

外文关键词：text sentiment analysis;self-attention mechanism;cross-attention mechanism;Transformer model;Long Short-Term Memory(LSTM)network

摘要：目前情感分析任务经常只聚焦于评论文本本身,忽略了评论者与被评论者的个体偏差特征,会显著影响对文本的整体情感判断。针对上述问题,提出一种融合评论双边个体偏差信息的文本情感分析模型UP-ATL(User and Product-Attention TranLSTM)。该模型使用自注意力机制、交叉注意力机制对评论文本与个体偏差信息分别进行双向融合,在融合过程中采用定制化权重的计算方式,以缓解实际应用场景中冷启动带来的数据稀疏问题,最终得到特征充分融合的评论文本和评论双边的表示信息。选取餐饮领域、电影领域的三个真实公开数据集Yelp2013、Yelp2014、IMDB进行效果验证,与UPNN(User Product Neural Network)、NSC(Neural Sentiment Classification)、CMA(Cascading Multiway Attention)、HUAPA(Hierarchical User And Product multi-head Attention)等基准模型进行比较。实验结果表明,相较于比较模型中最好的HUAPA模型,UP-ATL的准确度在三个数据集上依次分别提高了6.9、5.9和1.6个百分点。
However,current text sentiment analysis often focus on the comment text itself,but ignore individual bias information between commenters and commentees,which has a considerable impact on the overall sentiment analysis.A text sentiment analysis model based on individual bias information,named UP-ATL(User and Product-Attention TranLSTM),was proposed.In the model,self-attention mechanism and cross-attention mechanism were used to fuse the comment text and individual bias information in both directions.During the fusion process,a customized weight calculation method was used to alleviate the data sparsity problem caused by cold start in practical application scenarios.Finally,the feature fully fused comment text and bilateral representation information of the comment were obtained.Three real public datasets,Yelp2013,Yelp2014,and IMDB,were selected for effectiveness verification in the restaurant and film fields.The proposed model was compared with benchmark models such as UPNN(User Product Neural Network),NSC(Neural Sentiment Classification),CMA(Cascading Multiway Attention)and HUAPA(Hierarchical User And Product multi-head Attention).The experimental results show that compared to the previous best performing HUAPA model,the accuracy of UP-ATL increases by 6.9 percentage points,5.9 percentage points,and 1.6 percentage points,respectively on three datasets.