基于微博文本的个性化兴趣关注点及情绪变迁趋势研究

随着社交网络的快速发展,人们可利用微博平台发表、分享自己的观点以及抒发某种情绪,进而产生了大量针对不同话题的博文和情绪信息,但传统的文本挖掘算法在处理这些短小且具富含个性化情感信息的微博文本方面有所欠缺。在此提出一种基于微博文本的特征权重计算方法,可据此得到博主在不同时间段的关注点,通过情绪分类,分析用户在不同时间段内的情绪变迁情况。实验结果证明此方法具有一定的可行性。

Research on micro-blog based personal interests and emotion transition tendency

With the rapid development of social network, people can present or share their opinions and emotions on the micro-blog, and as a result, large amount of blog texts and emotional information of different topics are produced. As the traditional text mining algorithms cannot deal with these blog texts with fractional contents and different personal emotions effectively, this paper presents a novel weighting computation approach based on the key characters of micro-blog posts to get the dynamic interests of bloggers on different times. Furthermore, based on the emotion classification, the emotion transition tendency analysis can also be implemented. Experimental results show the feasibility of the presented approach.