A New Dataset for Evaluating Customer Satisfaction with Virtual Reality Headsets through Sentiment Analysis
Abstract
Virtual Reality (VR) is increasingly becoming a part of our daily lives, offering diverse applications from gaming and entertainment to education and training. VR utilizes pose tracking and 3D near-eye displays to create a simulated experience, immersing users in a virtual world. VR headsets use pose tracking and 3D near-eye displays to immerse users in virtual reality. They let users interact and explore virtual worlds by tracking their movements and displaying realistic 3D visuals close to their eyes and enable VR's simulated environments. The assessment of customers' satisfaction with VR headsets is important. Although there are several research in the context of VR and customers' preferences, this issue is fairly unexplored for customers' satisfaction with VR headsets using machine learning. The previous studies have mainly relied on user-based surveys with old-style approaches. Indeed, to explore the customers' satisfaction with these products, real-world datasets are necessary, yet the absence of such datasets poses a challenge. In this work, a new dataset is presented for the evaluation of customers' satisfaction with virtual reality headsets. The dataset is collected from the Amazon.com website. It includes 1123 online customers’ reviews on virtual reality headsets. Sentiment analysis is performed on online customers’ reviews to understand customers’ opinions regarding VR headsets. Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users’ opinions. Two predictive models, decision tree and XGBoost (extreme gradient boosting) predictor, are used to predict the sentiment class of the documents. The generated dataset can be utilized to gain a deeper understanding of customers' needs, preferences, and sentiments. This will enable the developers of VR headsets to produce more sophisticated products to meet customer expectations effectively.
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Arena, F., et al., An overview of augmented reality. Computers, 2022. 11(2): p. 28.
Billewar, S.R., et al., The rise of 3D E-Commerce: the online shopping gets real with virtual reality and augmented reality during COVID-19. World Journal of Engineering, 2022. 19(2): p. 244-253.
Huang, Y.-C., Integrated concepts of the UTAUT and TPB in virtual reality behavioral intention. Journal of Retailing and Consumer Services, 2023. 70: p. 103127.
Kim, G., B. Jin, and D.C. Shin, Virtual reality as a promotion tool for small independent stores. Journal of Retailing and Consumer Services, 2022. 64: p. 102822.
Kim, J.-H., et al., Immersive interactive technologies and virtual shopping experiences: Differences in consumer perceptions between augmented reality (AR) and virtual reality (VR). Telematics and Informatics, 2023. 77: p. 101936.
Ball, C., K.-T. Huang, and J. Francis, Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective. Telematics and Informatics, 2021. 65: p. 101728.
Schiopu, A.F., et al., Virus tinged? Exploring the facets of virtual reality use in tourism as a result of the COVID-19 pandemic. Telematics and Informatics, 2021. 60: p. 101575.
Hollebeek, L.D., et al., Virtual reality through the customer journey: Framework and propositions. Journal of Retailing and Consumer Services, 2020. 55: p. 102056.
Schnack, A., M.J. Wright, and J. Elms, Investigating the impact of shopper personality on behaviour in immersive Virtual Reality store environments. Journal of Retailing and Consumer Services, 2021. 61: p. 102581.
Poushneh, A., Augmented reality in retail: A trade-off between user's control of access to personal information and augmentation quality. Journal of Retailing and Consumer Services, 2018. 41: p. 169-176.
Aekanth, S.G., Transforming E-Learning Through the Use of Virtual and Augmented Reality: A Systematic Review. Human-Automation Interaction, 2023: p. 327-346.
Michalik, D., P. Kohl, and A. Kummert, Smart cities and innovations: Addressing user acceptance with virtual reality and Digital Twin City. IET Smart Cities, 2022. 4(4): p. 292-307.
Li, F., et al., Immersive technology-enabled digital transformation in transportation fields: A literature overview. Expert Systems with Applications, 2022. 202: p. 117459.
Turnbull, P.R. and J.R. Phillips, Ocular effects of virtual reality headset wear in young adults. Scientific reports, 2017. 7(1): p. 16172.
Hu, X., R. Su, and L. He. The design and implementation of the 3D educational game based on VR headsets. in 2016 International Symposium on Educational Technology (ISET). 2016. IEEE.
Tychsen, L. and P. Foeller, Effects of immersive virtual reality headset viewing on young children: Visuomotor function, postural stability, and motion sickness. American journal of ophthalmology, 2020. 209: p. 151-159.
Reer, F., et al., Virtual reality technology and game enjoyment: The contributions of natural mapping and need satisfaction. Computers in Human Behavior, 2022. 132: p. 107242.
Kim, D. and Y.J. Ko, The impact of virtual reality (VR) technology on sport spectators' flow experience and satisfaction. Computers in human behavior, 2019. 93: p. 346-356.
Bum, C.-H., T.Q. Mahoney, and C. Choi, A comparative analysis of satisfaction and sustainable participation in actual leisure sports and virtual reality leisure sports. Sustainability, 2018. 10(10): p. 3475.
Ahani, A., et al., Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 2019. 80: p. 52-77.
Ahani, A., et al., Evaluating medical travelers’ satisfaction through online review analysis. Journal of Hospitality and Tourism Management, 2021. 48: p. 519-537.
Nilashi, M., et al., The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers’ reviews. Heliyon, 2023. 9(11).
Nilashi, M., et al., What is the impact of service quality on customers’ satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics and Informatics, 2021. 64: p. 101693.
Nilashi, M., et al., Analysis of customers' satisfaction with baby products: The moderating role of brand image. Journal of Retailing and Consumer Services, 2023. 73: p. 103334.
Nilashi, M., et al., What factors influence students satisfaction in massive open online courses? Findings from user-generated content using educational data mining. Education and Information Technologies, 2022. 27(7): p. 9401-9435.
Nilashi, M., et al., Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 2019. 215: p. 767-783.
Nilashi, M., et al., Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services, 2021. 62: p. 102630.
Nilashi, M., et al., A proposed method for quality evaluation of COVID-19 reusable face mask. Measurement and Control, 2024. 57(6): p. 828-840.
Nilashi, M., et al., Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics, 2021. 61: p. 101597.
Nilashi, M., et al., Customer satisfaction analysis and preference prediction in historic sites through electronic word of mouth. Neural Computing and Applications, 2022. 34(16): p. 13867-13881.
Nilashi, M., et al., Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 2018. 28: p. 168-179.
Nilashi, M., et al., Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic commerce research and applications, 2016. 19: p. 70-84.
Nilashi, M., et al., A hybrid method with TOPSIS and machine learning techniques for sustainable development of green hotels considering online reviews. Sustainability, 2019. 11(21): p. 6013.
Nilashi, M., et al., Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches. Expert Systems with Applications, 2022. 199: p. 117092.
Nilashi, M., et al., An analytical approach for big social data analysis for customer decision-making in eco-friendly hotels. Expert Systems with Applications, 2021. 186: p. 115722.
Nilashi, M., et al., A new method for analysis of customers’ online review in medical tourism using fuzzy logic and text mining approaches. International Journal of Information Technology & Decision Making, 2022. 21(06): p. 1797-1820.
Nilashi, M., et al., Online reviews analysis for customer segmentation through dimensionality reduction and deep learning techniques. Arabian Journal for Science and Engineering, 2021. 46(9): p. 8697-8709.
Nilashi, M., et al., Analysis of travellers’ online reviews in social networking sites using fuzzy logic approach. International Journal of Fuzzy Systems, 2019. 21(5): p. 1367-1378.
Yadegaridehkordi, E., et al., Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society, 2021. 65: p. 101528.
Zibarzani, M., et al., Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology in Society, 2022. 70: p. 101977.
Nilashi, M., R.A. Abumalloh, and M. Zibarzani, Big social data analysis for quality of MOOC courses: the moderating role of practical examples. Quality & Quantity, 2025: p. 1-33.
Kumar, A., S. Chakraborty, and P.K. Bala, Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 2023. 73: p. 103363.
Ahani, A., et al., Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 2019. 51: p. 331-343.
Shafer, J., R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. in Vldb. 1996. Citeseer.
Nilashi, M., et al., Big social data analysis for impact of food quality on travelers’ satisfaction in eco-friendly hotels. ICT Express, 2023. 9(2): p. 182-188.
Nilashi, M., et al., An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering, 2017. 106: p. 212-223.
Nilashi, M., et al., Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach. Journal of Cleaner Production, 2019. 240: p. 118162.
Nilashi, M., et al., Travellers decision making through preferences learning: A case on Malaysian spa hotels in TripAdvisor. Computers & Industrial Engineering, 2021. 158: p. 107348.
Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.
Strobl, C., A.-L. Boulesteix, and T. Augustin, Unbiased split selection for classification trees based on the Gini index. Computational Statistics & Data Analysis, 2007. 52(1): p. 483-501.
Raileanu, L.E. and K. Stoffel, Theoretical comparison between the gini index and information gain criteria. Annals of Mathematics and Artificial Intelligence, 2004. 41: p. 77-93.
Ding, C.-S., et al. Stratified random sampling for power estimation. in Proceedings of International Conference on Computer Aided Design. 1996. IEEE.
de Vries, P.G. and P.G. de Vries, Stratified random sampling. Sampling Theory for Forest Inventory: A Teach-Yourself Course, 1986: p. 31-55.
Marom, N.D., L. Rokach, and A. Shmilovici. Using the confusion matrix for improving ensemble classifiers. in 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel. 2010. IEEE.
Freitas, C.O., et al. Confusion matrix disagreement for multiple classifiers. in Progress in Pattern Recognition, Image Analysis and Applications: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007. Proceedings 12. 2007. Springer.
Fawcett, T., An introduction to ROC analysis. Pattern recognition letters, 2006. 27(8): p. 861-874.
Pietraszek, T., On the use of ROC analysis for the optimization of abstaining classifiers. Machine learning, 2007. 68: p. 137-169.
Hand, D.J., Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine learning, 2009. 77(1): p. 103-123.
Vieira, S.M., U. Kaymak, and J.M. Sousa. Cohen's kappa coefficient as a performance measure for feature selection. in International conference on fuzzy systems. 2010. IEEE.
Cyr, L. and K. Francis, Measures of clinical agreement for nominal and categorical data: the kappa coefficient. Computers in biology and medicine, 1992. 22(4): p. 239-246.
Akbari, E., et al., ANFIS modeling for bacteria detection based on GNR biosensor. Journal of Chemical Technology & Biotechnology, 2016. 91(6): p. 1728-1736.
Foroughi, B., et al., Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 2023. 70: p. 103158.
Iranmanesh, M., et al., Factors influencing attitude and intention to use autonomous vehicles in Vietnam: findings from PLS-SEM and ANFIS. Information Technology & People, 2024. 37(6): p. 2223-2246.
Kheirandish, A., et al., Using ANFIS technique for PEM fuel cell electric bicycle prediction model. International Journal of Environmental Science and Technology, 2019. 16(11): p. 7319-7326.
Nilashi, M., Evaluation of Security Pillars in the Industrial Internet of Things: A Fuzzy Logic Approach. Journal of Soft Computing and Decision Support Systems, 2024. 11(4): p. 1-7.
Nilashi, M., et al., Accuracy analysis of Type-2 fuzzy system in predicting parkinson’s disease using biomedical voice measures. International Journal of Fuzzy Systems, 2024. 26(4): p. 1261-1284.
Nilashi, M., et al., A hybrid method to solve data sparsity in travel recommendation agents using fuzzy logic approach. Mathematical Problems in Engineering, 2022. 2022(1): p. 7372849.
Nilashi, M., et al., Research Article A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach. 2022.
Nilashi, M., et al., Early diagnosis of Parkinson’s disease: A combined method using deep learning and neuro-fuzzy techniques. Computational biology and chemistry, 2023. 102: p. 107788.
Nilashi, M., et al., A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 2019. 12(1): p. 13-20.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 2014. 41(8): p. 3879-3900.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 2014. 60: p. 82-101.
Nilashi, M., et al., A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 2015. 14(6): p. 542-562.
Nilashi, M., et al., Measuring country sustainability performance using ensembles of neuro-fuzzy technique. Sustainability, 2018. 10(8): p. 2707.
Nilashi, M., et al., A soft computing method for the prediction of energy performance of residential buildings. Measurement, 2017. 109: p. 268-280.
Nilashi, M., et al., An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset. Measurement, 2019. 136: p. 545-557.
Nilashi, M., et al., A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 2015. 19(11): p. 3173-3207.
Yadegaridehkordi, E., et al., Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 2018. 66: p. 364-386.
Zogaan, W.A., et al., A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features. MethodsX, 2024. 12: p. 102553.
Ahmadi, N., et al., Eye State Identification Utilizing EEG Signals: A Combined Method Using Self?Organizing Map and Deep Belief Network. Scientific Programming, 2022. 2022(1): p. 4439189.
Nilashi, M., et al., Predicting parkinson’s disease progression: Evaluation of ensemble methods in machine learning. Journal of healthcare engineering, 2022. 2022(1): p. 2793361.
Nilashi, M., et al., The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. Telematics and Informatics, 2023. 76: p. 101919.
Alghamdi, A., et al., Analysis of social data for accuracy improvement of collaborative filtering in MOOCs using text mining and deep learning techniques. Discover Computing, 2025. 28(1): p. 1-21.
Farokhi, M., et al., A Multi-Criteria Recommender System for Tourism Using Fuzzy Approach. Journal of Soft Computing & Decision Support Systems, 2016. 3(4).
Mardani, A., et al., A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 2020. 275: p. 122942.
Nilashi, M., et al., A combined method for diabetes mellitus diagnosis using deep learning, singular value decomposition, and self-organizing map approaches. Diagnostics, 2023. 13(10): p. 1821.
Nilashi, M., et al., Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques. Technology in Society, 2022. 71: p. 102118.
Nilashi, M., et al., A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques. Journal of Soft Computing & Decision Support Systems, 2016. 3(5).
Nilashi, M., et al., Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Information and Engineering, 2017. 9(3): p. 345-357.
Nilashi, M., et al., Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 2015. 293: p. 235-250.
Abumalloh, R.A., et al., The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education. Technology in Society, 2021. 67: p. 101728.
Abumalloh, R.A., et al., The adoption of metaverse in the retail industry and its impact on sustainable competitive advantage: moderating impact of sustainability commitment. Annals of Operations Research, 2024. 342(1): p. 5-46.
Al-Emran, M., et al., Determinants of using AI-based chatbots for knowledge sharing: evidence from PLS-SEM and fuzzy sets (fsQCA). IEEE Transactions on Engineering Management, 2023. 71: p. 4985-4999.
Asadi, S., et al., Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia. Journal of Cleaner Production, 2021. 282: p. 124474.
Foroughi, B., et al., Determinants of followers' purchase intentions toward brands endorsed by social media influencers: Findings from PLS and fsQCA. Journal of Consumer Behaviour, 2024. 23(2): p. 888-914.
Foroughi, B., et al., Determinants of continuance intention to use food delivery apps: findings from PLS and fsQCA. International journal of contemporary hospitality management, 2024. 36(4): p. 1235-1261.
Nilashi, M., et al., What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology. Telematics and Informatics, 2022. 69: p. 101795.
Nilashi, M., et al., How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry? Annals of Operations Research, 2025. 348(3): p. 1649-1690.
Saeidi, P., et al., The impact of enterprise risk management on competitive advantage by moderating role of information technology. Computer standards & interfaces, 2019. 63: p. 67-82.
Samad, S., et al., Green Supply Chain Management practices and impact on firm performance: The moderating effect of collaborative capability. Technology in Society, 2021. 67: p. 101766.
Samad, S., M. Nilashi, and O. Ibrahim, The impact of social networking sites on students’ social wellbeing and academic performance. Education and Information Technologies, 2019. 24(3): p. 2081-2094.
Yadegaridehkordi, E., et al., The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 2020. 40: p. 100921.
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