TY - JOUR
T1 - Data and text mining from online reviews
T2 - An automatic literature analysis
AU - Moro, Sérgio
AU - Rita, Paulo
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Moro, S., & Rita, P. (2022). Data and text mining from online reviews: An automatic literature analysis. WIREs: Data Mining and Knowledge Discovery, 12(3), 1-13. [e1448]. https://doi.org/10.1002/widm.1448 ---- Funding Information: The work by Sérgio Moro was supported by the Fundação para a Ciência e Tecnologia (FCT) within the following Projects: UIDB/04466/2020 and UIDP/04466/2020. The work by Paulo Rita was supported by the Fundação para a Ciência e a Tecnologia (FCT) within the Project: UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC).
PY - 2022/4/1
Y1 - 2022/4/1
N2 - This paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user-generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e-commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry.
AB - This paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user-generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e-commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry.
KW - consumer feedback
KW - data and text mining
KW - online reviews
KW - users' opinions
UR - http://www.scopus.com/inward/record.url?scp=85122965325&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000745780800001
U2 - 10.1002/widm.1448
DO - 10.1002/widm.1448
M3 - Review article
AN - SCOPUS:85122965325
SN - 1942-4787
VL - 12
SP - 1
EP - 13
JO - WIREs: Data Mining and Knowledge Discovery
JF - WIREs: Data Mining and Knowledge Discovery
IS - 3
M1 - e1448
ER -