面向微博文本流的负面情感突发话题检测

如何从海量、嘈杂的微博文本流中及时发现负面情感突发话题对于突发事件的应急响应和处置至关重要，而传统的突发话题检测方法往往忽略了负面情感突发话题与非负面情感突发话题之间的区别，为此提出了一种面向微博文本流的负面情感突发话题检测（NE-BTD）算法。首先，将微博中的主题词对的加速度和负面情感强度变化率作为负面情感突发话题的判定依据；然后，利用突发词对的速度确定负面情感突发话题的窗口范围；最后，使用一种基于吉布斯采样的狄利克雷多项式混合模型（GSDMM）聚类算法得到窗口中负面情感突发话题的主题结构。在实验中将所提出的NE-BTD算法与已有的一种基于情感方法的话题检测（EBM-TD）算法进行对比，结果表明所提出的NE-BTD算法相较EBM-TD算法准确率和召回率至少提高了20%，并且可以至少提前40 min检出负面情感突发话题。

Detection of negative emotion burst topic in microblog text stream

How to find negative emotion burst topic in time from massive and noisy microblog text stream is essential for emergency response and handling of emergencies. However, the traditional burst topic detection methods often ignore the differences between negative emotion burst topic and non-negative emotion burst topic. Therefore, a Negative Emotion Burst Topic Detection (NE-BTD) algorithm for microblog text stream was proposed. Firstly, the accelerations of keyword pairs in microblog and the change rate of negative emotion intensity were used as the basis for judging the topics of negative emotion. Secondly, the speeds of burst word pairs were used to determine the window range of negative emotion burst topics. Finally, a Gibbs Sampling Dirichlet Multinomial Mixture model (GSDMM) clustering algorithm was used to obtain the topic structures of the negative emotion burst topics in the window. In the experiments, the proposed NE-BTD algorithm was compared with an existing Emotion-Based Method of Topic Detection (EBM-TD) algorithm. The results show that the NE-BTD algorithm was at least 20% higher in accuracy and recall than the EBM-TD algorithm, and it can detect negative emotion burst topic at least 40 minutes earlier.