基于性格情绪特征的改进主题情感模型

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。

An Improved Topic Sentiment Model Based on User Character

In the sentiment analysis in micro-blogs, most existing topic sentiment models do not fully consider the users personality characteristics. Based on the JST model, this paper proposes a time-based personality modeling method to incorporate users 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.