TY - GEN
T1 - A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS
AU - Nakayama, Hiroki
AU - Katagaya, Masashi
AU - Onuma, Ryo
AU - Kaminaga, Hiroaki
AU - Miyadera, Youzou
AU - Nakamura, Shoich
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - Extractions of Web articles
KW - SNS
KW - Twitter
KW - Understanding topic transitions
KW - Web visualization
UR - http://www.scopus.com/inward/record.url?scp=85080967098&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080967098&partnerID=8YFLogxK
U2 - 10.1109/ICBDA47563.2019.8987128
DO - 10.1109/ICBDA47563.2019.8987128
M3 - Conference contribution
AN - SCOPUS:85080967098
T3 - 2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
SP - 19
EP - 23
BT - 2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
Y2 - 19 November 2019 through 21 November 2019
ER -