Impact Statement:Soft computing is increasingly applied in recommender systems and sentiment analysis. This topic-based bibliometric study indicated a constantly expanding community and a...Show More
Abstract:
Soft computing, which focuses on approximate models and provides solutions to complicated real-life issues, has gained increasing momentum in application-specific domains...Show MoreMetadata
Impact Statement:
Soft computing is increasingly applied in recommender systems and sentiment analysis. This topic-based bibliometric study indicated a constantly expanding community and a continuingly growing scientific output in this area. Throughout the years, scholars generally showed more interest in recommendations via soft computing. However, recent years have witnessed an increasing interest in aspect-based sentiment analysis, emotion detection through short text and minor/domain language, and dialect mining in social media assisted by natural language processing and feature representation. We also revealed future efforts on model validation for real-life implementations, model generalization to diverse domains and contexts, sentiment dynamics, simultaneous modeling of diverse sentiment categories and aspects, discrete and sparse data, additional information use, and the use of advanced deep learning algorithms.
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 ...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 3, Issue: 5, October 2022)