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

doi:10.11772/j.issn.1001-9081.2015.10.2905

计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2905-2910.DOI: 10.11772/j.issn.1001-9081.2015.10.2905

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

李超雄¹, 黄发良¹, 温肖谦¹, 李璇¹, 元昌安²

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

收稿日期:2015-04-13 修回日期:2015-06-16 出版日期:2015-10-10 发布日期:2015-10-14
通讯作者: 黄发良(1975-),男,湖南永州人,副教授,博士,CCF会员,主要研究方向:数据挖掘、智能信息系统,huangfliang@163.com
作者简介:李超雄(1991-),男,福建莆田人,硕士研究生,主要研究方向:数据挖掘、知识发现;温肖谦(1989-),女,福建莆田人,硕士研究生,主要研究方向:数据挖掘;李璇(1984-),女,湖北黄石人,讲师,博士,主要研究方向:信息安全;元昌安(1964-),男,安徽肥东人,教授,博士,CCF会员,主要研究方向:智能计算、数据挖掘。
基金资助:
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)。

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

LI Chaoxiong¹, HUANG Faliang¹, WEN Xiaoqian¹, LI Xuan¹, YUAN Chang'an²

1. Faculty of Software, Fujian Normal University, Fuzhou Fujian 350007, China;
2. College of Computer and Information Engineering, Guangxi Teachers Education University, Nanning Guangxi 530023, China

Received:2015-04-13 Revised:2015-06-16 Online:2015-10-10 Published:2015-10-14

摘要/Abstract

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。

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

Abstract: 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.

Key words: topic-sentiment evolution, sentiment mining, microblog, Latent Dirichlet Allocation (LDA), emotional cycle

中图分类号:

TP393.1

李超雄, 黄发良, 温肖谦, 李璇, 元昌安. 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[J]. 计算机应用, 2015, 35(10): 2905-2910.

LI Chaoxiong, HUANG Faliang, WEN Xiaoqian, LI Xuan, YUAN Chang'an. Evolution analysis method of microblog topic-sentiment based on dynamic topic sentiment combining model[J]. Journal of Computer Applications, 2015, 35(10): 2905-2910.

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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

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