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.
|Title of host publication||HCI International 2019 – Late Breaking Posters - 21st HCI International Conference, HCII 2019, Proceedings|
|Editors||Constantine Stephanidis, Margherita Antona|
|Number of pages||10|
|Publication status||Published - 2019|
|Event||21st International Conference on Human Computer Interaction, HCII 2019 - Orlando, United States|
Duration: 2019 Jul 26 → 2019 Jul 31
|Name||Communications in Computer and Information Science|
|Conference||21st International Conference on Human Computer Interaction, HCII 2019|
|Period||19/7/26 → 19/7/31|
Bibliographical noteFunding Information:
Acknowledgements. This work was conducted with the support of the Design Engineering Postgraduate Schools (N0001436) program, an R&D project initiated by the Ministry of Trade, Industry and Energy of the Republic of Korea.
© 2019, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Computer Science(all)