Open Access

ARTICLE

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

by Seon Hong Lee¹, Haein Lee¹, Jang Hyun Kim^2,*

1 Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, 03063, Korea
2 Department of Interaction Science/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, 03063, Korea

* Corresponding Author: Jang Hyun Kim. Email:

Computers, Materials & Continua 2022, 72(3), 4983-4997. https://doi.org/10.32604/cmc.2022.027943

Received 29 January 2022; Accepted 08 March 2022; Issue published 21 April 2022

Abstract

Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of mobile applications. To overcome this limitation, several researchers perform user experience analysis through online reviews and star ratings. However, some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text. This variation disturbs the performance of machine learning. To alleviate the inconsistencies, Valence Aware Dictionary and sEntiment Reasoner (VADER), which is a sentiment classifier based on lexicon, is introduced. The current study aims to build a more accurate sentiment classifier based on machine learning with VADER. In this study, five sentiment classifiers are used, such as Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression, Light Gradient Boosting Machine (LightGBM), and Categorical boosting algorithm (Catboost) with three embedding methods (Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec). The results show that classifiers that apply VADER outperform those that do not apply VADER, excluding one classifier (Logistic Regression with Word2Vec). Moreover, LightGBM with TF-IDF has the highest accuracy 88.68% among other models.

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Keywords

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