摘要
【目的】构建一种微博舆情热点的监控和预测模型,从话题聚类及情感强度的角度出发解决短文本漂移、情感极性量化等问题。【方法】提出一种基于话题聚类及情感强度的微博舆情分析模型,实现微博话题快速聚类及情感强度量化计算,通过时序回归分析追踪预测热点话题的情感变化。【结果】实验结果表明,本文模型预测准确率达88.97%,对比i Lab-Edinburgh模型提高约7%,证明了模型的可行性。【局限】未考虑突发事件下,模型对于事件的预警预测效果。【结论】本文模型能够有效提高公众情感倾向的预测准确性,为微博舆情分析方法提供新的途径。
[Objective] This paper builds a model to monitor the trending topics from microblogs, aiming to deal with the issues of text drifting and quantitation of sentimental polarity. [Methods] First, we proposed a public opinion analysis model based on topic clustering and sentiment intensity. Then, we used the time series regression analysis to predict the sentimental changes among the trending topics. [Results] The prediction accuracy of our model reached 88.97%, which was about 7% higher than the i Lab-Edinburgh model. [Limitations] More research is needed to study the early warning mechanisms for emergency events. [Conclusions] The proposed model could improve the prediction accuracy of sentimental changes, which provides an effective way to analyze the public opinion from microblogs.
作者
王秀芳
盛姝
路燕
Wang Xiufang;Sheng Shu;Lu Yan(College of Computer of Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,Chin)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2018年第6期37-47,共11页
Data Analysis and Knowledge Discovery
基金
山东科技大学2018年研究生科技创新项目"一种基于话题聚类及情感强度的微博舆情分析模型"(项目编号:SDKDYC180222)的研究成果之一
关键词
舆情分析
情感分析
话题聚类
情感强度分析
Public Opinion
Analysis Sentiment
Analysis Topic Clustering
Sentiment Intensity Analysis
