李玉强,黄瑜,孙念,李琳,刘爱华. 基于性格情绪特征的改进主题情感模型[J]. 中文信息学报, 2020, 34(7): 96-104.
LI Yuqiang, HUANG Yu, SUN Nian, LI Lin, LIU Aihua. An Improved Topic Sentiment Model Based on User Character. , 2020, 34(7): 96-104.
An Improved Topic Sentiment Model Based on User Character
LI Yuqiang1, HUANG Yu1, SUN Nian1, LI Lin1, LIU Aihua2
1.School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430063, China; 2.School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, China
摘要近年来,以微博为代表的社交媒体在情感分析中备受关注。然而,绝大多数现有的主题情感模型并没有充分考虑到用户性格特征,导致情感分析结果难尽人意。故该文在现有的JST模型基础上进行改进,提出一种基于时间的性格建模方法,将用户性格特征纳入主题情感模型中;鉴于微博数据包含大量的表情符号之类的特有信息,为了充分利用表情符号来提升微博情感识别性能,该文将情感符号融入JST模型中,进而提出了一种改进的主题情感联合模型UC-JST(Joint Sentiment/Topic Model Based on User Character)。通过在真实的新浪微博数据集上进行实验,结果表明UC-JST情感分类效果优于JST、TUS-LDA、JUST、TSMMF四种典型的无监督情感分类方法。
Abstract：In the sentiment analysis in micro-blogs, most existing topic sentiment models do not fully consider the users personality characteristics. Based on the JST model, this paper proposes a time-based personality modeling method to incorporate users personality features into the topic sentiment model. Since the microblog data contains a lot of unique information such as emoticons, we also introduce emoticons into the JST model. As a result, an probabilistic model named UC-JST(Joint Sentimet/Topic model based on User Character)is proposed. Tested on the real Sina Weibo dataset, the results show that UC-JST performs better than JST, TUS-LDA ,JUST and TSMMF in terms of sentiment classification accuracy.
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2020, Vol. 34 
