城市画像视角下的社会公众情感演化研究  

Analyzing Public Sentiments from the Perspective of City Profiles

作　　者：叶光辉[1] 曾杰妍 胡婧岚 毕崇武 Ye Guanghui;Zeng Jieyan;Hu Jinglan;Bi Chongwu(School of Information Management,Central China Normal University,Wuhan 430079,China)

机构地区：[1]华中师范大学信息管理学院,武汉430079

出　　处：《数据分析与知识发现》2020年第4期15-26,共12页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目“基于标签语义挖掘的城市画像计算与应用模型研究”(项目编号:71804055);湖北省自然科学基金项目“基于社会化标签挖掘的智慧城市‘印象云’构建模式研究”(项目编号:2018CFB387);中央高校基本科研业务费项目“基于社会化标签挖掘的城市画像研究”(项目编号:CCNU18QN040)的研究成果之一。

摘　　要：【目的】为及时掌握城市动态、引导公众舆论、识别与预测城市潜在问题,构建城市画像视角下的公众情感演化模型。【方法】通过LDA2Vec提取时间窗口下的城市主题;采用基于词典的情感分析方法细粒度划分城市主题的情感类别,并计算各个城市主题的情感强度;最后利用TF-IDF算法追踪时间序列下引起公众情感演化的城市事件,并构建ARMA模型预测公众情感趋势。【结果】本文模型中"好"的情感强度预测准确率达97%,"恶"的情感强度预测准确率达90%,证明了模型的可行性。【局限】未将突发事件作为影响因素加入公众情感演化模型。【结论】本文方法能够有效识别和判断引起公众情感极性发生变化的城市事件,并具有较高的情感强度预测准确性。[Objective] This study constructs an evolution model for social sentiment analysis from the perspective of city profiles, aiming to grasp city dynamics, guide public opinions, as well as identify and predict potential issues. [Methods] We firstly used the LDA2 Vec algorithm to extract city themes from each time window. Then,we applied a dictionary-based sentiment analysis method to fine-grain the emotion categories of city themes, and calculated their emotional intensities. Finally, we tracked city events arising changes of public sentiments with the TF-IDF algorithm, and built the ARMA model to predict social sentiment trends. [Results] Our model’s accuracy rate for predicting emotional intensity of"like"reached 97%, while those of the"dislike"scores were up to 90%.[Limitations] We did not include unexpected events as an influencing factor to the proposed model.[Conclusions] Our method could effectively identify city events and predict emotional changes of public opinions.

关 键 词：城市画像 情感演化 LDA2Vec 舆情监控 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]