A bibliometric review of soft computing for recommender systems and sentiment analysis

Xieling CHEN, Haoran XIE, Jingjing WANG, Zongxi LI, Gary CHENG, Man Leung WONG, Fu Lee WANG

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

4 Citations (Scopus)

Abstract

Soft computing, which focuses on approximate models and provides solutions to complicated real-life issues, has gained increasing momentum in application-specific domains, such as sentiment analysis and recommender systems, to emulate cognitive processes behind decision-making. In this work, bibliometrics and structural topic modeling (STM) were adopted to analyze the text contents of research articles concerning soft computing for sentiment analysis and recommender systems. Results indicated that this research field had experienced a dramatic increase in both quantity and quality as measured by scientific output and their received citations. Using STM, we identified 17 research topics frequently discussed within the analyzed articles. The analysis of annual topic prevalence indicated a shift in research foci from recommender applications to sentiment analysis and a growing interest in soft computing. This study served as a guideline for those seeking to contribute to research on soft computing for sentiment analysis and recommender systems. We also made methodological contributions by combining the leading-edge text mining algorithms to make the time-honored bibliometrics adaptive to the analysis of large quantities of unstructured texts beyond structured publication data statistics.

Original languageEnglish
Pages (from-to)642-656
Number of pages15
JournalIEEE Transactions on Artificial Intelligence
Volume3
Issue number5
Early online date4 Oct 2021
DOIs
Publication statusPublished - 1 Oct 2022

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Bibliometric analysis
  • recommendation systems
  • sentiment analysis
  • soft computing
  • structural topic modeling (STM)

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