基于性格的微博情感分析模型PLSTM

Personality-based microblog sentiment analysis model PLSTM

不同性格用户所具有的语言表达方式不尽相同,现有情感分析工作很少考虑到用户性格,针对此问题,提出一种基于性格的微博情感分析模型PLSTM。该模型首先采用性格识别规则将微博文本分为五个性格集合和一个通用集合,其次针对每种性格文本集合分别训练出一个情感分类器,最后对六个基本情感分类器进行融合,得出最终的情感极性。实验结果显示PLSTM方法的F1值可以达到96.95%,表明PLSTM比起基准情感分析模型在准确率、召回率、F1值上都有较大提高。

Users of different personalities have different language expressions.Existing sentiment analysis work rarely considers the personality of the user.To solve this problem,this paper proposed a micro-blog sentiment analysis model based personality,called PLSTM.The model firstly used the personality recognition rules to divide the microblog text into five personality sets and a universal set,then trained a sentiment classifier for each personality set,and finally integrated six basic sentiment classifiers to obtain the ultimate sentiment polarity.The experimental results show that the F1 value of the PLSTM method can reach 96.95%,which indicates that PLSTM has a higher improvement in accuracy,recall rate and F1 value than the commonly used benchmark sentiment analysis model.