Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost
As the business world shifts to the web and tremendous amounts of data become available on multilingual mobile applications, new business and research challenges and opportunities have been explored. This research aims to intensify the usage of data analytics, machine learning, and sentiment analysi...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2370.pdf |
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| author | Dana A. Al-Qudah Ala’ M. Al-Zoubi Alexandra I. Cristea Juan J. Merelo-Guervós Pedro A. Castillo Hossam Faris |
| author_facet | Dana A. Al-Qudah Ala’ M. Al-Zoubi Alexandra I. Cristea Juan J. Merelo-Guervós Pedro A. Castillo Hossam Faris |
| author_sort | Dana A. Al-Qudah |
| collection | DOAJ |
| description | As the business world shifts to the web and tremendous amounts of data become available on multilingual mobile applications, new business and research challenges and opportunities have been explored. This research aims to intensify the usage of data analytics, machine learning, and sentiment analysis of textual data to classify customers’ reviews, feedback, and ratings of businesses in Jordan’s food and restaurant industry. The main methods used in this research were sentiment polarity (to address the challenges posed by businesses to automatically apply text analysis) and bio-metric techniques (to systematically identify users’ emotional states, so reviews can be thoroughly understood). The research was extended to deal with reviews in Arabic, dialectic Arabic, and English, with the main focus on the Arabic language, as the application examined (Talabat) is based in Jordan. Arabic and English reviews were collected from the application, and a new model was proposed to sentimentally analyze reviews. The proposed model has four main stages: data collection, data preparation, model building, and model evaluation. The main purpose of this research is to study the problem expressed above using a model of ordinal regression to overcome issues related to misclassification. Additionally, an automatic multi-language prediction approach for online restaurant reviews was proposed by combining the eXtreme gradient boosting (XGBoost) and particle swarm optimization (PSO) techniques for the ordinal regression of these reviews. The proposed PSO-XGB algorithm showed superior results when compared to support vector machine (SVM) and other optimization methods in terms of root mean square error (RMSE) for the English and Arabic datasets. Specifically, for the Arabic dataset, PSO-XGB achieved an RMSE value of 0.7722, whereas PSO-SVM achieved an RSME value of 0.9988. |
| format | Article |
| id | doaj-art-83fdfdb4a2774de6a01b123d2e8be35b |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-83fdfdb4a2774de6a01b123d2e8be35b2025-01-11T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e237010.7717/peerj-cs.2370Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoostDana A. Al-Qudah0Ala’ M. Al-Zoubi1Alexandra I. Cristea2Juan J. Merelo-Guervós3Pedro A. Castillo4Hossam Faris5King Abdullah II School for Information Technology, The University of Jordan, Amman, JordanFaculty of Information Technology, Applied Science Private University, Amman, JordanDepartment of Computer Science, Durham University, Durham, United KingdomDepartment of Computer Architecture and Technology, Universidad de Granada, Granada, SpainDepartment of Computer Architecture and Technology, Universidad de Granada, Granada, SpainKing Abdullah II School for Information Technology, The University of Jordan, Amman, JordanAs the business world shifts to the web and tremendous amounts of data become available on multilingual mobile applications, new business and research challenges and opportunities have been explored. This research aims to intensify the usage of data analytics, machine learning, and sentiment analysis of textual data to classify customers’ reviews, feedback, and ratings of businesses in Jordan’s food and restaurant industry. The main methods used in this research were sentiment polarity (to address the challenges posed by businesses to automatically apply text analysis) and bio-metric techniques (to systematically identify users’ emotional states, so reviews can be thoroughly understood). The research was extended to deal with reviews in Arabic, dialectic Arabic, and English, with the main focus on the Arabic language, as the application examined (Talabat) is based in Jordan. Arabic and English reviews were collected from the application, and a new model was proposed to sentimentally analyze reviews. The proposed model has four main stages: data collection, data preparation, model building, and model evaluation. The main purpose of this research is to study the problem expressed above using a model of ordinal regression to overcome issues related to misclassification. Additionally, an automatic multi-language prediction approach for online restaurant reviews was proposed by combining the eXtreme gradient boosting (XGBoost) and particle swarm optimization (PSO) techniques for the ordinal regression of these reviews. The proposed PSO-XGB algorithm showed superior results when compared to support vector machine (SVM) and other optimization methods in terms of root mean square error (RMSE) for the English and Arabic datasets. Specifically, for the Arabic dataset, PSO-XGB achieved an RMSE value of 0.7722, whereas PSO-SVM achieved an RSME value of 0.9988.https://peerj.com/articles/cs-2370.pdfOrdinal regressionSentiment polarityEvolutionaryParticle swarm optimisationXGBoost |
| spellingShingle | Dana A. Al-Qudah Ala’ M. Al-Zoubi Alexandra I. Cristea Juan J. Merelo-Guervós Pedro A. Castillo Hossam Faris Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost PeerJ Computer Science Ordinal regression Sentiment polarity Evolutionary Particle swarm optimisation XGBoost |
| title | Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost |
| title_full | Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost |
| title_fullStr | Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost |
| title_full_unstemmed | Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost |
| title_short | Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost |
| title_sort | prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary xgboost |
| topic | Ordinal regression Sentiment polarity Evolutionary Particle swarm optimisation XGBoost |
| url | https://peerj.com/articles/cs-2370.pdf |
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