基于OTSCM模型的主题情感在线追踪

网络舆论主题情感在线分析对舆情研判与管理起着十分重要的作用，当前的主题情感模型存在着主题与情感建模关系不紧密，情感挖掘偏斜等问题，容易造成舆情误判。文本在OLDA（On-Line Latent Dirichlet Allocation，OLDA）模型的基础上引入情感参数，并提出情感遗传思想，建立基于情感遗传的在线主题情感混合模型OTSCM（On-Line Topic and Sentiment Combining Model）。该模型把t-1时间片内的主题情感分布作为t时间片内主题情感分布的先验，通过构造主题情感演化矩阵，生成t时间片内文档—主题、主题—特征词以及主题—情感词3个分布，最后使用交叉熵方法计算t时间片内主题分布与t-1之前主题分布的相似度，得出t时间片内主题情感演化结果。本文在5个数据集上对OTSCM进行了验证，并与其它流行算法进行了对比，实验表明，文本方法在主题情感在线识别方面达到了良好的效果。

OTSCM Approach for Tracking On-Line Sentiment of Topic

The on-line sentiment analysis of network topic plays an important role in the evaluation and management of public opinion.The current topic and sentiment models have a problem that the relationship between the topic and sentiment is not closely,which likely cause the deviation of sentiment mining and misjudgment of public opinion.This paper introduced the sentiment parameter into OLDA model and proposed a On-Line Topic and Sentiment Combining Model (OTSCM) based on sentiment genetic.This model made the topic and sentiment distribution of the t-1 time slice as a priori of the topic and sentiment distribution of t time slice.By constructing the topic and sentiment evolutionary matrix,the document-topic,topic-word and topic-sentiment 3 distributions were generated.The cross entropy method was used to calculate the similarity between the topic distribution of the t time slice and the t-1 time slice for getting the evolutionary result of t time slice.At last,OTSCM were validated on 5 data sets and compared with other state-of-the-art algorithms.Experiments showed that our approach had better performance.