User-Generated Content Extraction: A Bibliometric Analysis of the Research Literature (2007–2022)

Abstract

Scientific studies on user-generated content extraction began in 2007. User-generated content (UGC), which is all forms of content created by someone, is widely available on social media and can influence customer desire to shop. This study aims to systematically map research trends in the field of UGC extraction over the last 15 years using metadata taken from the Scopus database. Thus, novelties and opportunities will be found that will serve as a resource for researchers conducting research and determining the research theme. Bibliometric review analysis was carried out in this study by analyzing literature from year 2007 until 2022. The search using keywords related to UGC extraction resulted in 382 papers related to the specified keywords. The main findings of this study are 1) Research in the field of UGC extraction has emerged and has grown since 2007, 2) Research in this field has been conducted by researchers from various countries, mostly from China, followed by the United States, India, Italy, Germany, Spain, etc., 3) Several keywords were discussed in this field, which include UGC, sentiment analysis, opinion mining, social media, and information extraction. This bibliometric analysis has provided information on research opportunities/directions related to UGC extraction in the future. The originality of this study is that a bibliometric analysis was performed for the research trends in UGC with a focus on technical extraction. This topic is interesting to raise because mining and extracting knowledge from UGC is quite an expensive and labor-intensive undertaking.