A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS

Hiroki Nakayama, Masashi Katagaya, Ryo Onuma, Hiroaki Kaminaga, Youzou Miyadera, Shoich Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-23
Number of pages5
ISBN (Electronic)9781728133089
DOIs
Publication statusPublished - 2019 Nov
Event2019 IEEE Conference on Big Data and Analytics, ICBDA 2019 - Penang, Malaysia
Duration: 2019 Nov 192019 Nov 21

Publication series

Name2019 IEEE Conference on Big Data and Analytics, ICBDA 2019

Conference

Conference2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
CountryMalaysia
CityPenang
Period19/11/1919/11/21

Keywords

  • Extractions of Web articles
  • SNS
  • Twitter
  • Understanding topic transitions
  • Web visualization

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Decision Sciences (miscellaneous)

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  • Cite this

    Nakayama, H., Katagaya, M., Onuma, R., Kaminaga, H., Miyadera, Y., & Nakamura, S. (2019). A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS. In 2019 IEEE Conference on Big Data and Analytics, ICBDA 2019 (pp. 19-23). [8987128] (2019 IEEE Conference on Big Data and Analytics, ICBDA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA47563.2019.8987128