A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS
A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS
复制标题
一种根据SNS焦点文章把握网络话题转移的支持方法
DOI：
10.1109/icbda47563.2019.8987128
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shoichi Nakamura
中科院分区：
文献类型：
--
作者：
Hiroki Nakayama;Masashi Katagaya;Ryo Onuma;Hiroaki Kaminaga;Youzou Miyadera;Shoichi Nakamura
近年来，由于高性能电子终端和Twitter等社交网络服务（SNS）的普及，许多人以网络文章的形式在线发布信息变得更加容易。浏览各种信息的机会也增加了。因此，用户常常从许多文章中收集到的信息不足，并且需要了解信息中包含的主题。然而，他们很难找到与自己感兴趣的主题相关的文章并确定文章中的主题转换。因此，本研究旨在开发新的支持来理解与用户感兴趣的主题相关的文章以及 SNS 上文章的主题转换。在本文中，我们提出了一种基于 Twitter 时间线分析来提取与感兴趣的文章相关的主题词的方法。此外，我们提出了一种在分析Web文章词性的基础上提取与主题进展相关的Web文章的方法。此外，我们进行了实验以评估所提出方法的有用性，并从实验结果中获得了发现。
In recent years, it has become easier for many people to post information online in the form of Web articles due to the popularization of high-performance electronic terminals and social networking services (SNSs) such as Twitter. Opportunities for browsing a wide variety of information have also increased. As a result, users often collect insufficient pieces of information from many articles and need to understand the topics contained in the information. However, it is difficult for them to find articles related to the topics that they are interested in and determine topic transitions in the articles. Therefore, this research is aimed at developing novel support for understanding articles related to a topic that a user is interested in and the topic transitions from articles on SNSs. In this paper, we propose a method for extracting topic words related to an article of interest on the basis of an analysis of timelines on Twitter. Moreover, we propose a method for extracting Web articles related to the progress of topics on the basis of an analysis of parts of speech in Web articles. Furthermore, we conducted experiments in order to evaluate the usefulness of the proposed methods and acquired findings from the experimental results.