中文摘要：

为了提升微博话题发现效率以及发现质量问题，提出了一种融入公众情感投入分析的微博话题快速发现与细分方法，促使话题演化，进而产生新话题及其情感变化趋势。首先，基于情感词典和TFDF值在历史语料库中挖掘常用情感词并构建情感词库；其次，快速抽取情感文本，结合Sigmoid函数检测情感投入密集期，保证话题事件挖掘的质量；最后，通过改进的模糊C-均值聚类算法在新的微博数据中发现高质量话题。实验结果表明，本文方法能够有效提升移动环境下的话题发现效率及质量。

英文摘要：

To improve the discovery efficiency and quality of micro-blog topic, a method of rapid discovery and segmentation in micro-blog topics based on public emotional engagement analysis was proposed, it would prompt evolution of the topics, then generate new topics and gain emotional change trend. Firstly, common emotional words were mined from corpus to build emotional thesaurus based on emotional word dictionary and TFDF. Then, emotional text was extracted quickly and sigmoid function was utilized to detect the intensive period of emotional engagement, ensuring the validity of topic mining. Besides, an improved adaptive FCM was used to cluster and discover topics. The experimental results show that this method can enhanee the efficiency and quality of topic discovery in mobile environment.