An Online Comment Assistant for a Better Comment Experience

Ju Yeon Choi; Younah Kang; Keeheon Lee

doi:10.1007/978-3-030-30712-7_45

An Online Comment Assistant for a Better Comment Experience

Ju Yeon Choi, Younah Kang, Keeheon Lee

Techno-Art Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we examined online news comment activities that are used as a key means of public opinion and interaction in modern society. We extracted keywords in comments using big data and sorted them according to frequency. Through this process, a chatbot was designed to check the ranking and contents of comments based on keywords represent the most important issues in the comment population and search for classified comments according to keywords before users read the news. Through experiments using this chatbot, we compared and analyzed the nature of the comments in the existing comment system and the characteristics of the system experience. Results shows that the chatbot designed in this study can provide more information than existing comment presentation systems, search for comments by type, and check more information than articles. In addition, assessing the nature of the existing comments reduced the number of sensational, non-slang-oriented comments.

Original language	English
Title of host publication	HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings
Editors	Constantine Stephanidis, Margherita Antona
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	359-368
Number of pages	10
ISBN (Print)	9783030307110
DOIs	https://doi.org/10.1007/978-3-030-30712-7_45
Publication status	Published - 2019
Event	21st International Conference on Human Computer Interaction, HCII 2019 - Orlando, United States Duration: 2019 Jul 26 → 2019 Jul 31

Publication series

Name	Communications in Computer and Information Science
Volume	1088
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st International Conference on Human Computer Interaction, HCII 2019
Country/Territory	United States
City	Orlando
Period	19/7/26 → 19/7/31

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-30712-7_45

Cite this

Choi, J. Y., Kang, Y., & Lee, K. (2019). An Online Comment Assistant for a Better Comment Experience. In C. Stephanidis, & M. Antona (Eds.), HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings (pp. 359-368). (Communications in Computer and Information Science; Vol. 1088). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-30712-7_45

Choi, Ju Yeon ; Kang, Younah ; Lee, Keeheon. / An Online Comment Assistant for a Better Comment Experience. HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings. editor / Constantine Stephanidis ; Margherita Antona. Springer Science and Business Media Deutschland GmbH, 2019. pp. 359-368 (Communications in Computer and Information Science).

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title = "An Online Comment Assistant for a Better Comment Experience",

abstract = "In this study, we examined online news comment activities that are used as a key means of public opinion and interaction in modern society. We extracted keywords in comments using big data and sorted them according to frequency. Through this process, a chatbot was designed to check the ranking and contents of comments based on keywords represent the most important issues in the comment population and search for classified comments according to keywords before users read the news. Through experiments using this chatbot, we compared and analyzed the nature of the comments in the existing comment system and the characteristics of the system experience. Results shows that the chatbot designed in this study can provide more information than existing comment presentation systems, search for comments by type, and check more information than articles. In addition, assessing the nature of the existing comments reduced the number of sensational, non-slang-oriented comments.",

keywords = "Chatbot, Comment analysis, Comment curation, Data-driven user experience",

author = "Choi, {Ju Yeon} and Younah Kang and Keeheon Lee",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 21st International Conference on Human Computer Interaction, HCII 2019 ; Conference date: 26-07-2019 Through 31-07-2019",

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doi = "10.1007/978-3-030-30712-7_45",

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Choi, JY, Kang, Y & Lee, K 2019, An Online Comment Assistant for a Better Comment Experience. in C Stephanidis & M Antona (eds), HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings. Communications in Computer and Information Science, vol. 1088, Springer Science and Business Media Deutschland GmbH, pp. 359-368, 21st International Conference on Human Computer Interaction, HCII 2019, Orlando, United States, 19/7/26. https://doi.org/10.1007/978-3-030-30712-7_45

An Online Comment Assistant for a Better Comment Experience. / Choi, Ju Yeon; Kang, Younah ; Lee, Keeheon.
HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings. ed. / Constantine Stephanidis; Margherita Antona. Springer Science and Business Media Deutschland GmbH, 2019. p. 359-368 (Communications in Computer and Information Science; Vol. 1088).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this study, we examined online news comment activities that are used as a key means of public opinion and interaction in modern society. We extracted keywords in comments using big data and sorted them according to frequency. Through this process, a chatbot was designed to check the ranking and contents of comments based on keywords represent the most important issues in the comment population and search for classified comments according to keywords before users read the news. Through experiments using this chatbot, we compared and analyzed the nature of the comments in the existing comment system and the characteristics of the system experience. Results shows that the chatbot designed in this study can provide more information than existing comment presentation systems, search for comments by type, and check more information than articles. In addition, assessing the nature of the existing comments reduced the number of sensational, non-slang-oriented comments.

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Choi JY, Kang Y , Lee K. An Online Comment Assistant for a Better Comment Experience. In Stephanidis C, Antona M, editors, HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 359-368. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-30712-7_45

An Online Comment Assistant for a Better Comment Experience

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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