中文摘要：

微博情感分析是社交媒体挖掘中的重要任务之一,在个性化推荐、舆情分析等方面具有重要的理论和应用价值.挖掘性能良好且可同步进行文档主题分析与情感分析的主题情感模型,近年来在以微博为代表的社交媒体情感分析中备受关注.然而,绝大多数现有主题情感模型都只简单地假设不同微博的情感极性是互相独立的,这与微博生态的现实状况不相一致,从而导致这些模型无法对用户的真实情感进行有效建模.基于此,综合考虑了微博用户相互关联的事实,提出了基于LDA和微博用户关系的主题情感模型SRTSM（social relation topic sentiment model）.该模型在LDA中加入情感层与微博用户关系参数,利用微博用户关系与微博主题学习微博的情感极性.针对新浪微博真实数据集上的大量实验结果表明：与代表性算法JST,Sentiment-LDA及DPLDA相比较,SRTSM模型能够对用户真实情感与讨论主题进行更加有效的分析建模.

英文摘要：

Sentiment analysis in micro-blogging is an important task in mining social media, and has important theoretical and application value in personalized recommendation and public opinion analysis. Topic sentiment models have attracted much attention due to their good performance and ability of synchronized topic and the sentiment analysis in micro-blogs. However, most existing models simply assume that topic sentiment distributions of different micro-blogs are independent, which is contrary to the realistic status in micro-blogging and thus further leads to unsatisfactory modeling of micro-blogger＇s true sentiment. To address the issues, a probabilistic model, SRTSM （social relation topic sentiment model） is proposed. The new model introduces sentiment and miero-blogger social relation into LDA inference framework and achieves synchronized detection of sentiment and topic in micro-blogging. Extensive experiments on Sina Weibo show that SRTSM outperforms state-of-the-art unsupervised approaches including JST, SLDA and DPLDA significantly in terms of sentiment classification accuracy.