基于动态主题情感混合模型的微博主题情感演化分析方法  

Evolution analysis method of microblog topic-sentiment based on dynamic topic sentiment combining model

作　　者：李超雄[1] 黄发良[1] 温肖谦 李璇[1] 元昌安[2] 

机构地区：[1]福建师范大学软件学院,福州350007 [2]广西师范学院计算机与信息工程学院,南宁530023

出　　处：《计算机应用》2015年第10期2905-2910,共6页journal of Computer Applications

基　　金：教育部人文社会科学研究青年基金资助项目(12YJCZH074);福建省教育厅A类项目(JA13077)

摘　　要：针对现有模型无法进行微博主题情感演化分析的问题,提出一种基于主题情感混合模型(TSCM)和情感周期性理论的主题情感演化模型——动态主题情感混合模型(DTSCM)。DTSCM通过捕获不同时间片中微博消息集的主题和情感,追踪不同时间片内主题与情感的变化趋势,获得主题情感演化图,从而实现主题和情感的演化分析。真实微博数据集上的实验结果表明,与当前优秀代表算法JST(Joint Sentiment/Topic)、S-LDA(Sentiment-Latent Dirichlet Allocation)和DPLDA(Dependency Phrases-Latent Dirichlet Allocation)相比,该方法的情感分类准确率分别提高了3.01%、4.33%和8.75%,并且可以获得主题情感演化图。这表明该方法具有更高的情感分类准确率并且可以进行微博主题情感演化分析,为舆情分析等应用提供了较好的帮助。For the problem of existing models＇ disability to analyze topic-sentiment evolution of microblogs, a Dynamic Topic Sentiment Combining Model （DTSCM） was proposed based on Topic Sentiment Combining Model （TSCM） and the emotional cycle theory. DTSCM could track the topic sentiment evolution trend and obtain the graph of topic sentiment evolution so as to analyze the evolution of topic and sentiment by capturing the topic and sentiment of microblogs in different time. The experimental results in real microblog corpus showed that, in contrast with state-of-the-art models Joint Sentiment/ Topic （JST）, Sentiment-Latent Dirichlet Allocation （S-LDA） and Dependency Phrases-Latent Dirichlet Allocation （ DPLDA）, the sentiment classification accuracy of DTSCM increased by 3.01%, 4.33% and 8.75% respectively, and DTSCM could obtain topic-sentiment evolution of microblogs. The proposed approach can not only achieve higher sentiment classification accuracy but also analyze topic-sentiment evolution of microblog, and it is helpful for public opinion analysis.

关 键 词：主题情感演化 情感挖掘 微博 潜在狄利克雷分配 情感周期性 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]