基于动态主题—情感演化模型的网络舆情信息分析

Analysis of Online Public Opinion Information Based on the Dynamic Theme-emotion Evolution Model

【目的/意义】目前,静态情感倾向判断成为分析舆情信息的一种重要手段,但这种方法局限于最终的情感分类结果,不能追溯到整个情感演变过程以及各阶段的影响因素,因此无法提出更为细致和有针对性的措施。【方法/过程】鉴于此,本文提出一种基于动态主题-情感演化模型的舆情信息分析方法,通过对评论文本进行语义角色标注,建立情感单元词表;然后将改进的TF-IDF和K-Means聚类方法相结合提取主题词,形成主题-情感匹配词表,比起传统的TF-IDF方法,其准确率和F值都有明显提升;最后引入时间节点,利用点互信息(Pointwise Mutual Information,PMI)和情感词典的方法,进行动态情感演化分析。【结果/结论】实验研究证明,该方法得出的情感演化趋势与实际情况相吻合,为进一步制定治理网络舆情危机的措施,提供了有效依据。

【Purpose/significance】At present,the judgment of static emotional tendency has become an important method for the analysis of public opinion information,but this method is limited to the final emotion classification results,and cannot be traced back to the whole process of emotion evolution and the influencing factors of each stage,so it can't put forward more detailed and targeted measures.【Method/process】This paper proposes a public opinion information analysis method based on the dynamic theme-emotion evolution model.Through semantic role labeling on the comment text,the emotion unit word list is established.Then the improved TF-IDF and K-Means clustering method are combined to extract the subject words and form the theme-emotion matching word list.Compared with the traditional TF-IDF method,the accuracy and F value are significantly improved.Finally,time nodes are introduced to analyze the dynamic emotional evolution using the method of Pointwise Mutual Information(PMI)and the dictionary of emotion.【Result/conclusion】Experimental research proves that the trend of emotion evolution obtained by this method is consistent with the actual situation,which provides effective basis for further developing measures to govern online public opinion crisis.