基于用户性格和语义-结构特征的文本评论情感分类方法  

A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features

作　　者：王友卫[1] 刘瑞 凤丽洲 WANG You-wei;LIU Rui;FENG Li-zhou(School of Information,Central University of Finance and Economics,Beijing 100081,China;School of Statistics,Tianjin University of Finance and Economics,Tianjin 300222,China)

机构地区：[1]中央财经大学信息学院,北京100081 [2]天津财经大学统计学院,天津300222

出　　处：《电子学报》2024年第5期1657-1669,共13页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61906220);教育部人文社科资助项目(No.19YJCZH178);国家社会科学基金(No.18CTJ008);中央财经大学新兴交叉学科建设项目。

摘　　要：由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments,BF_Bi GAC).依据大五人格模型能够有效表达用户性格的优势,通过计算不同维度性格得分,从评论文本中获取用户性格特征.利用双向门控循环单元(Bidirectional Gated Recurrent Unit,Bi GRU)和卷积神经网络(Convolutional Neural Network,CNN)可以有效提取文本上下文语义特征和局部结构特征的优势,提出一种基于Bi GRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响,引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合,以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明,BF_Bi GAC在分类准确率(Accuracy)和加权macro F_(1)值(F_(w))上均获得较好表现,相对于拼接Bi GRU、CNN的情感分类方法(Sentiment Classification Method Concatenating Bi GRU and CNN,Bi G-RU_CNN)在Accuracy值上分别提升0.020、0.012、0.017及0.011,相对于拼接CNN、Bi GRU的情感分类方法(Sentiment Classification Method Concatenating CNN and Bi GRU,Conv Bi LSTM)F_(w)值上分别提升0.022、0.013、0.028及0.023;相对于预训练模型BERT和Ro BERTa,BF_Bi GAC在保证分类精度的情况下获得了较高的运行效率.Since the traditional sentiment classification methods for text comments usually ignore the influence of user personality on sentiment classification results,a sentiment classification method for text comments based on user personality and semantic-structural features is proposed.According to the advantage of Big Five personality model on effectively expressing the user personality,the user personality feature is obtained from the comment texts by calculating the personality scores from different dimensions.Moreover,the advantages of bidirectional gated recurrent unit(BiGRU)and convolutional neural network(CNN)on effectively extracting the contextual semantic features and the local structural features are taken,and a new text semantic-structural feature acquisition method based on BiGRU,CNN and two-layer attention mechanism is proposed.Finally,in order to distinguish the influence of the features with different types,the hybrid attention layer is introduced to obtain the final text vector representation by integrating the user personality feature and the textural semantic-structural feature effectively.The experimental results on the datasets of IMDB,Yelp-2,Yelp-5 and Ekman show that BF_BiGAC achieves good performance when the measurements of Accuracy and weighted macro F_(1)(F_(w))are used.Specifically,it achieves the improvements of 0.020,0.012,0.017 and 0.011 compared to sentiment classification method concatenating BiGRU and CNN(BiGRU_CNN)on accuracy,and achieves the improvements of 0.022,0.013,0.028 and 0.023 compared to sentiment classification method concatenating CNN and BiGRU(ConvBiLSTM)on F_(w).Moreover,when comparing with the pre-trained models of BERT and RoBERTa,BF_BiGAC achieves higher executing efficiency while ensuring the classification accuracy.

关 键 词：情感分类 大五人格模型 双向门控循环单元 卷积神经网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]