文章基本信息

标题：Analysis of Flaming and Its Applications in CGM
本地全文：下载
作者：Yuki Iwasaki ; Ryohei Orihara ; Yuichi Sei 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2015
卷号：30
期号：1
页码：152-160
DOI：10.1527/tjsai.30.152
出版社：The Japanese Society for Artificial Intelligence
摘要：Nowadays, anybody can easily express their opinion publicly through Consumer Generated Media. Because of this, a phenomenon of flooding criticism on the Internet, called flaming, frequently occurs. Although there are strong demands for flaming management, a service to reduce damage caused by a flaming after one occurs, it is very difficult to properly do so in practice. We are trying to keep the flaming from happening. It is necessary to identify the situation and the remark which are likely to cause flaming for our goal. Concretely, we propose methods to identify a potential tweet which will be a likely candidate of a flaming on Twitter, considering public opinion among Twitter users. Among three categories of flamings, our main focus is Struggles between Conflicting Values (SBCV), which is defined as a remark that forces one's own opinion about a topic on others. Forecasting of this type of flamings is potentially desired since most of its victims are celebrities, who need to care one's own social images. We proceed with a working hypothesis: a SBCV is caused by a gap between the polarity of the remark and that of public opinion . First, we have visualized the process how a remark gets flamed when its content is far from public opinion, by means of our original parameter daily polarity (dp) . Second, we have built a highly accurate flaming prediction model with decision tree learning, using cumulative dp as an attribute along with parameters available from Twitter APIs. The experimental result suggests that the hypothesis is correct.
关键词：flaming ; microblogging ; reputation mining ; decision tree ; sentiment analysis