不同情绪对网络群体极化影响的实证研究——基于VAR模型
发布时间：2018-08-06 13:22
【摘要】：为了研究网络群体中不同情绪对群体极化的影响,本文从网络上已发生的群体极化现象出发,使用python抓取2016年8月17日到2016年10月8日期间的41496825条微博评论作为数据样本,并通过Stanford Word Segmenter进行文本分词,然后使用LIWC进行文本分析,进而结合群体极化的测量方法,建立VAR模型。研究发现:负向情绪比正向情绪更容易引起网络群体极化;相比于悲伤情绪和焦虑情绪,愤怒情绪更容易引起网络群体极化。最后,为企业处理公关危机以及政府应对舆论舆情等提供决策建议。
[Abstract]:In order to study the effect of different emotions on group polarization, this paper uses python to capture 41496825 Weibo comments from August 17, 2016 to October 8, 2016 as data samples from the phenomenon of group polarization that has taken place on the network. The text segmentation is carried out by Stanford Word Segmenter, then text analysis is carried out by LIWC, and then the VAR model is established by combining the measurement method of population polarization. It is found that negative emotion is more likely to cause network population polarization than positive emotion, and anger is more likely to cause network population polarization than sadness and anxiety. Finally, for enterprises to deal with public relations crisis and government response to public opinion and other decision-making advice.
【作者单位】： 中南大学商学院;
【基金】：国家自然科学基金资助项目“SNS环境中消费者如何卷入网络团购:群体对个体影响的视角”(71272066)
【分类号】：B842.6
本文编号：2167859
[Abstract]:In order to study the effect of different emotions on group polarization, this paper uses python to capture 41496825 Weibo comments from August 17, 2016 to October 8, 2016 as data samples from the phenomenon of group polarization that has taken place on the network. The text segmentation is carried out by Stanford Word Segmenter, then text analysis is carried out by LIWC, and then the VAR model is established by combining the measurement method of population polarization. It is found that negative emotion is more likely to cause network population polarization than positive emotion, and anger is more likely to cause network population polarization than sadness and anxiety. Finally, for enterprises to deal with public relations crisis and government response to public opinion and other decision-making advice.
【作者单位】： 中南大学商学院;
【基金】：国家自然科学基金资助项目“SNS环境中消费者如何卷入网络团购:群体对个体影响的视角”(71272066)
【分类号】：B842.6
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