TY - GEN
T1 - A Data Analytics Methodology for Benchmarking of Sentiment Scoring Algorithms in the Analysis of Customer Reviews
AU - Abou-Kassem, Tesneem
AU - Alazeezi, Fatima Hamad Obaid
AU - Ertek, Gurdal
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - Due to the digitalization, there exists an increased amount of user-generated content on the Internet, where people express their opinions on various topics. Sentiment analysis is the statistical and analytical examination of human emotions and opinions regarding a certain subject. Our study extends the literature by developing a data analytics methodology for the benchmarking of sentiment scoring algorithms in the context of online customer reviews. We demonstrate the applicability of the methodology using Amazon product reviews as the source data. Analyzing text-based content such as Amazon customers’ reviews through text analytics and sentiment analysis can help Amazon and other online retailers to discover valuable actionable insights regarding their products. The contributions of this study are twofolds: to examine the predictive power of machine learning (ML) algorithms with respect to predicting sentiment scores and to analyze patterns in the differences between scores obtained from different sentiment scoring algorithms.
AB - Due to the digitalization, there exists an increased amount of user-generated content on the Internet, where people express their opinions on various topics. Sentiment analysis is the statistical and analytical examination of human emotions and opinions regarding a certain subject. Our study extends the literature by developing a data analytics methodology for the benchmarking of sentiment scoring algorithms in the context of online customer reviews. We demonstrate the applicability of the methodology using Amazon product reviews as the source data. Analyzing text-based content such as Amazon customers’ reviews through text analytics and sentiment analysis can help Amazon and other online retailers to discover valuable actionable insights regarding their products. The contributions of this study are twofolds: to examine the predictive power of machine learning (ML) algorithms with respect to predicting sentiment scores and to analyze patterns in the differences between scores obtained from different sentiment scoring algorithms.
KW - Gap analysis
KW - Machine learning
KW - Online customer reviews
KW - Sentiment analysis
KW - Text analytics
UR - http://www.scopus.com/inward/record.url?scp=85174718834&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174718834&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-3243-6_46
DO - 10.1007/978-981-99-3243-6_46
M3 - Conference contribution
AN - SCOPUS:85174718834
SN - 9789819932429
T3 - Lecture Notes in Networks and Systems
SP - 569
EP - 581
BT - Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
A2 - Yang, Xin-She
A2 - Sherratt, R. Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Congress on Information and Communication Technology, ICICT 2023
Y2 - 20 February 2023 through 23 February 2023
ER -