Abstract—Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%.
Index Terms—Machine learning, prediction, QoUE, users opinion mining, web crawling.
Cosmas Ifeanyi Nwakanma, Md. Sajjad Hossain, Jae-Min Lee, and Dong-Seong Kim are with the Networked Systems Laboratory, IT Convergence Engineering, School of Electronics Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk 39177, South Korea; Cosmas Ifeanyi Nwakanma is also with the Department of Information Technology, School of Information and Communication Technology, Federal University of Technology, Owerri, Nigeria (Corresponding author: Dong-Seong Kim; e-mail: email@example.com).
Cite: Cosmas Ifeanyi Nwakanma, Md. Sajjad Hossain, Jae-Min Lee, and Dong-Seong Kim, "Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 752-758, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).