基于转移变量的图文融合微博情感分析  

Multimedia sentiment analysis on micro-blog based on transition variable

作　　者：邓佩 谭长庚[1] Deng Pei;Tan Changgeng(School of Software,Central South University,Changsha 410075,China)

机构地区：[1]中南大学软件学院,长沙410075

出　　处：《计算机应用研究》2018年第7期2038-2041,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(61602525)

摘　　要：针对传统的微博情感分析方法忽略了图片影响因素、特殊符号信息以及上下文信息导致情感分析方法准确率不高的问题,提出了一种基于转移变量的图文融合微博情感分析方法。首先构建基于转移变量的无监督情感分析模型USAMTV来分析文本情感分布,通过引入连词情感转移变量和转发符号主题转移变量来处理句子的情感从属和主题从属,获得文本的情感分布;然后引入图片因素为情感浓度来影响文本的情感分布;最后计算微博的整体情感倾向。与JST和ASUM模型相比,本模型测试数据集的准确率更高。实验结果表明,该方法能更准确地预测微博情感倾向。Since the traditional methods of micro-blog sentiment analysis ignore the influence of images,the special symbol information and the context semantic information,which leads to the accuracy rate of the emotion analysis method is not high.Therefore,this paper presented a micro-blog sentiment method,which was illustrated and based on transferred variable. Firstly,it paper constructed an unsupervised sentiment analysis model USAMTV,which was based on transferred variable,to analysis the distribution of emotion in the text. That was to say,the emotion and topic were categorized by introducing the sentimental transferred variable of conjunction and thematic transferred variable of reposting symbols. Then it introduced the image factors as emotional weight to decide the emotional distribution of the text. Finally,it obtained the overall sentimental orientation of the micro-blog. Compared with the JST model and the ASUM model,this proposed method had higher accuracy in the real-world test dataset. The experimental results show that the proposed method can predict the emotional tendency of microblog more accurately.

关 键 词：情感分析 图文融合 转移变量 转发符号 主题模型 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]