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Research Article

Classification of Customer Reviews Using Machine Learning Algorithms

References

  • Ali, F., K. Kwak, and Y. Kim. 2016. Opinion mining based on fuzzy domain ontology and support vector machine: A proposal to automate online review classification. Applied Soft Computing 47:235–50. doi:10.1016/j.asoc.2016.06.003.
  • Al-Smadi, M., O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta. 2018. Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science 27:386–93. doi:10.1016/j.jocs.2017.11.006.
  • Bahassine, S., A. Madani, M. Al-Sarem, and M. Kissi. 2018. Feature selection using an improved Chi-square for Arabic text classification. Journal of King Saud University-Computer and Information Sciences 32(2):225-231. https://doi.org/10.1016/j.jksuci.2018.05.010
  • Bi, J., Y. Liu, Z. Fan, and E. Cambria. 2019. Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research 57(22):1–21. https://doi.org/10.1080/00207543.2019.1574989
  • Cambria E., D. Das, S. Bandyopadhyay, and A. Feraco. 2017. Affective computing and sentiment analysis. In A Practical Guide to Sentiment Analysis. Socio-Affective Computing vol 5., ed. Cambria E., Das D., Bandyopadhyay S., Feraco A., vol 5. Cham: Springer. https://doi.org/10.1007/978-3-319-55394-8_1
  • Cao, Q., W. Duan, and Q. Gan. 2011. Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems 50:511–21. doi:10.1016/j.dss.2010.11.009.
  • Cardot, H., and D. Degras. 2018. Online principal component analysis in high dimension: Which algorithm to choose? International Statistical Review 86:29–50. doi:10.1111/insr.12220.
  • Dadgar, S., M. Araghi, and M. Farahani 2016. A novel text mining approach based on TF-IDF and support vector machine for news classification. In A novel text mining approach based on TF-IDF and support vector machine for news classification. 2016 IEEE International Conference on Engineering and Technology (ICETECH), IEEE, Coimbatore, India. 112–16.
  • Dickinger, A., and J. Mazanec. 2015. Significant word items in hotel guest reviews: A feature extraction approach. Tourism Recreation Research 40:353–63. doi:10.1080/02508281.2015.1079964.
  • Duan, W., Y. Yu, Q. Cao, and S. Levy. 2016. Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly 57:282–96. doi:10.1177/1938965515620483.
  • Ducange, P., R. Pecori, and P. Mezzina. 2018. A glimpse on big data analytics in the framework of marketing strategies. Soft Computing 22:325–42. doi:10.1007/s00500-017-2536-4.
  • Fu, Y., J. Hao, X. Li, C. Hsu, X. Li, C. Hsu, and C. Hsu. 2018. Predictive accuracy of sentiment analytics for tourism: A metalearning perspective on Chinese travel news. Journal of Travel Research 57:0047287518772361. doi:10.1177/0047287517700317.
  • Gulsoy, N., and S. Kulluk. 2019. A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9:e1299.
  • Hu, Y., and K. Chen. 2016. Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management 36:929–44. doi:10.1016/j.ijinfomgt.2016.06.003.
  • Ma, E., M. Cheng, and A. Hsiao. 2018. Sentiment analysis–a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management 30:3287–308. doi:10.1108/IJCHM-10-2017-0704.
  • Manek, A., P. Shenoy, M. Mohan, and K. Venugopal. 2017. Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web 20:135–54. doi:10.1007/s11280-015-0381-x.
  • Moro, S., P. Rita, and J. Coelho. 2017. Stripping customers’ feedback on hotels through data mining: The case of Las Vegas Strip. Tourism Management Perspectives 23:41–52. doi:10.1016/j.tmp.2017.04.003.
  • Sánchez-Franco, M., A. Navarro-García, and F. Rondán-Cataluña. 2019. A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of Business Research 101:499-506.
  • Schuckert, M., X. Liu, and R. Law. 2015. Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing 32(5): 608–621. https://doi.org/10.1080/10548408.2014
  • Talón-Ballestero, P., L. González-Serrano, C. Soguero-Ruiz, S. Muñoz-Romero, and J. Rojo-Álvarez. 2018. Using big data from customer relationship management information systems to determine the client profile in the hotel sector. Tourism Management 68:187–97. doi:10.1016/j.tourman.2018.03.017.
  • Tran, T., H. Ba, and V. Huynh 2019. Measuring hotel review sentiment: An aspect-based sentiment analysis approach. In: Seki H., Nguyen C., Huynh V. N., Inuiguchi M. (eds) Integrated uncertainty in knowledge modelling and decision making. IUKM 2019. Lecture notes in computer science, vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_33
  • Tripathy, A., A. Agrawal, and S. Rath. 2016. Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications 57:117–26. doi:10.1016/j.eswa.2016.03.028.
  • Uğuz, H. 2011. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems 24:1024–32. doi:10.1016/j.knosys.2011.04.014.
  • Uysal, A., and S. Gunal. 2014. The impact of preprocessing on text classification. Information Processing & Management 50:104–12. doi:10.1016/j.ipm.2013.08.006.
  • Wang, F., C. Li, J. Wang, J. Xu, and L. Li. 2015. A two-stage feature selection method for text categorization by using category correlation degree and latent semantic indexing. Journal of Shanghai Jiaotong University (Science) 20:44–50. doi:10.1007/s12204-015-1586-y.
  • Wang, H., P. Yin, J. Yao, and J. Liu. 2013. Text feature selection for sentiment classification of Chinese online reviews. Journal of Experimental & Theoretical Artificial Intelligence 25:425–39. doi:10.1080/0952813X.2012.721139.
  • Ye, Q., Z. Zhang, and R. Law. 2009. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications 36:6527–35. doi:10.1016/j.eswa.2008.07.035.
  • Zainuddin, N., A. Selamat, and R. Ibrahim. 2018. Hybrid sentiment classification on twitter aspect-based sentiment analysis. Applied Intelligence 48:1218–1232.

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