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A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naive Bayes were modeled for sentiment and emotion analyses, wherea...

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Main Authors: Balakrishnan, Vimala, Lok, Pik Yin, Abdul Rahim, Hajar
Format: Article
Published: Springer Verlag 2021
Subjects:
QA75 Electronic computers. Computer science
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/27117/
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spelling my.um.eprints.271172022-05-11T08:34:27Z http://eprints.um.edu.my/27117/ A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews Balakrishnan, Vimala Lok, Pik Yin Abdul Rahim, Hajar QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naive Bayes were modeled for sentiment and emotion analyses, whereas latent Dirichlet allocation was administered to identify top emerging topics based on English textual reviews from three digital payment applications. Random forest produced the best results for sentiment (F1 score = 73.8%; Cohen's Kappa = 52.2%) and emotion (F1 score = 58.8%; Cohen's Kappa = 44.7%) analyses based on a tenfold cross-validation. Latent Dirichlet allocation revealed best clusters atk = 5 and items = 25, with the top topics being App Service, Transaction, Reload Features, Connectivity and Reward. Findings are presented and discussed in general and also based on each application. Springer Verlag 2021-04 Article PeerReviewed Balakrishnan, Vimala and Lok, Pik Yin and Abdul Rahim, Hajar (2021) A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews. The Journal of Supercomputing, 77 (4). pp. 3795-3810. ISSN 0920-8542, DOI https://doi.org/10.1007/s11227-020-03412-w <https://doi.org/10.1007/s11227-020-03412-w>. 10.1007/s11227-020-03412-w
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
TA Engineering (General). Civil engineering (General)
Balakrishnan, Vimala
Lok, Pik Yin
Abdul Rahim, Hajar
A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
description This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naive Bayes were modeled for sentiment and emotion analyses, whereas latent Dirichlet allocation was administered to identify top emerging topics based on English textual reviews from three digital payment applications. Random forest produced the best results for sentiment (F1 score = 73.8%; Cohen's Kappa = 52.2%) and emotion (F1 score = 58.8%; Cohen's Kappa = 44.7%) analyses based on a tenfold cross-validation. Latent Dirichlet allocation revealed best clusters atk = 5 and items = 25, with the top topics being App Service, Transaction, Reload Features, Connectivity and Reward. Findings are presented and discussed in general and also based on each application.
format Article
author Balakrishnan, Vimala
Lok, Pik Yin
Abdul Rahim, Hajar
author_facet Balakrishnan, Vimala
Lok, Pik Yin
Abdul Rahim, Hajar
author_sort Balakrishnan, Vimala
title A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
title_short A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
title_full A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
title_fullStr A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
title_full_unstemmed A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
title_sort semi-supervised approach in detecting sentiment and emotion based on digital payment reviews
publisher Springer Verlag
publishDate 2021
url http://eprints.um.edu.my/27117/
_version_ 1735409501686726656
score 13.211869

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