A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis

Xieling CHEN, Haoran XIE*, Gary CHENG, Zongxi LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

17 Citations (Scopus)


Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide insights into the issues addressed during the past decade and the potential future of sentic computing. We combined bibliometric analysis and structural topic modeling to examine sentic computing literature in various aspects, including the tendency of annual article count, top journals, countries/regions, institutions, and authors, the scientific collaborations between major contributors, as well as the major topics and their tendencies. We obtained interesting and meaningful findings. For example, sentic computing has attracted growing interest in academia. In addition, Cognitive Computation and Nanyang Technological University were found to be the most productive journal and institution in publishing sentic computing studies, respectively. Moreover, important issues such as cyber issues and public opinion, deep neural networks and personality, financial applications and user profiles, and affective and emotional computing have been commonly addressed by authors focusing on sentic computing. Our study provides a thorough overview of sentic computing, reveals major concerns among scholars during the past decade, and offers insights into the future directions of sentic computing research.
Original languageEnglish
Pages (from-to)24-47
Number of pages24
JournalCognitive Computation
Issue number3
Early online date5 May 2021
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
This study was funded by One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19–20), the Research Cluster Fund (RG 78/2019-2020R), and the Interdisciplinary Research Scheme of the Dean’s Research Fund 2019–20 (FLASS/DRF/IDS-2) of The Education University of Hong Kong, and the HKIBS Research Seed Fund 2019/20 (190–009), the Direct Grant (101138), the Lam Woo Research Fund (LWI20011) and the Research Seed Fund (102367) of Lingnan University, Hong Kong.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Bibliometric analysis
  • Sentic computing
  • Structural topic modeling
  • Topic detection and evolution analysis


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