research-article

Prediction of the customers' interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages

Authors:

Bihter DasAuthors Info & Claims

Volume 35, Issue 3

Pages 227 - 237

https://doi.org/10.1016/j.jksuci.2023.02.017

Published: 01 March 2023 Publication History

Abstract

In the business world, large companies that can achieve continuity in innovation gain a significant competitive advantage. The sensitivity of these companies to follow and monitor news sources in e-commerce, social media, and forums provides important information to businesses in the decision-making process. With the large amount of data shared in these resources, sentiment analysis can be made from people's comments about services and products, users' emotions can be extracted and important feedback can be obtained. All of this is of course possible with accurate sentiment analysis. In this study, new data sets were created for Turkish, English, and Arabic, and for the first time, comparative sentiment analysis was performed from texts in three different languages. In addition, a very comprehensive study was presented to the researchers by comparing the performances of both the pre-trained language models for Turkish, Arabic, and English, as well as the deep learning and machine learning models. Our paper will guide researchers working on sentiment analysis about which methods will be more successful in texts written in different languages, which contain different types and spelling mistakes, which factors will affect the success, and how much these factors will affect the performance.

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Cited By

Al-Saadawi HDas BDas R(2024)A systematic review of trimodal affective computing approachesExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124852255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124852
Jain AJain GTewari D(2024)KNetwork: advancing cross-lingual sentiment analysis for enhanced decision-making in linguistically diverse environmentsKnowledge and Information Systems10.1007/s10115-023-02051-w66:5(2925-2943)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10115-023-02051-w

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cover image Journal of King Saud University - Computer and Information Sciences

Journal of King Saud University - Computer and Information Sciences Volume 35, Issue 3

Mar 2023

302 pages

ISSN:1319-1578

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Elsevier Science Inc.

United States

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Published: 01 March 2023

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Cited By

Al-Saadawi HDas BDas R(2024)A systematic review of trimodal affective computing approachesExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124852255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124852
Jain AJain GTewari D(2024)KNetwork: advancing cross-lingual sentiment analysis for enhanced decision-making in linguistically diverse environmentsKnowledge and Information Systems10.1007/s10115-023-02051-w66:5(2925-2943)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10115-023-02051-w

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