基于多特征融合的评论文本个性化情感分类新方法  

Multi-feature Fusion Based New Personalized Sentiment Classification Method for Comment Texts

作　　者：王友卫[1] 刘奥 凤丽洲 WANG Youwei;LIU Ao;FENG Lizhou(School of Information,Central University of Finance and Economics,Beijing 100081,China;School of Science and Engineering,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：《计算机科学》2023年第S02期87-93,共7页Computer Science

基　　金：国家自然科学基金(61906220);教育部人文社科项目(19YJCZH178);国家社科基金(18CTJ008);中央财经大学新兴交叉学科建设项目。

摘　　要：现有的情感分类研究未能充分考虑用户个人历史评论中蕴含的个性特征对情感分类结果的影响,且未能综合考虑用户社会关系、个人属性、历史评论与当前评论等诸多因素的共同作用。为此,提出一种基于多特征融合的评论文本个性化情感分类新方法。首先,利用大量无标注的用户历史评论挖掘用户个性表达,结合用户历史评论和用户属性信息提取得到用户特征向量;然后,利用node2vec算法在获得图节点表示方面的优势对用户社会关系网络进行学习以得到用户的社会关系向量,并利用预训练的word2vec模型获得用户当前评论向量;最后,将用户特征向量、社会关系向量和有标注的当前评论向量输入全连接神经网络中进行训练以得到最终的分类模型。在从中文股吧爬取的真实数据集上的实验结果表明,与支持向量机、朴素贝叶斯、TextCNN、Bert等典型方法相比,所提方法能够有效提高情感分类的准确率和F 1值,验证了其在改善情感分类表现方面的有效性。Existing research on sentiment classification fails to fully consider the influence of personality characteristics contained in user’s personal historical comments on the results of sentiment classification,and fails to comprehensively consider the combined effects of many factors such as user’s social relations,personal attributes,historical comments and current comments.To this end,a new personalized method for sentiment classification of comment texts based on multi-feature fusion is proposed.First,the user’s personality expressions is mined by using a great number of unlabeled user’s historical comments,and the user’s feature vector is extracted by combining user’s historical comments and attribute information.Then,the advantages of the node2vec algorithm in obtaining the node representation of the graph are used to learn users’social relationship networks,so as to obtain the users’social relationship vectors,and the pre-trained word2vec model is used to obtain the user’s current comment vector.Finally,the user’s feature vector,social relationship vector and labeled current comment vector are entered into the fully connected classifier for training to obtain the final classification model.Experimental results on the real data set crawled from the Chinese stock page show that compared with typical methods such as support vector machine,naive Bayes,TextCNN,Bert,the proposed method can effectively improve the accuracy and F 1 value of sentiment classification,which verifies its effectiveness in improving sentiment classification performance.

关 键 词：情感分类 股票评论 社会关系 历史评论 全连接神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]