Download pdf
Sentiment Analysis and Rating Video Game Dimensions via NLP
- 1 School of Software Engineering, South China University of Technology, Guangzhou, China
- 2 International Business School, Chongqing Technology and Business University, Chongqing, 400067, China
- 3 Nanjing No.1 High School, Nanjing, China
- 4 Shenghua Zizhu Academy, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
Online gaming platforms generate vast amounts of user comments, which serve as valuable information for potential users and purchasers. Extracting meaningful guiding information from this data is crucial. As a Natural Language Processing technique (referred to as NLP), sentiment analysis technology demonstrates a high efficiency in accurately discerning the emotional tones within the comments, thereby objectively evaluating the advantages and disadvantages of gaming products based on user comments. In this paper, the comments of two games are analyzed, Grand Theft Auto V (referred to as GTAV) and Cyberpunk 2077 (referred to as 2077) on the Steam platform. Based on the self-built sentiment dictionary, keywords representing different sentiments were extracted by applying two different sentiment analysis models which are VADER and TEXTBLOB. Then we apply two models to classify the sentiments that the keywords express. Finally, the ratings of five dimensions (community, gameplay, storyline, sound and graphic) were obtained by transforming the sentiment analyze score, and the differences in the training results of different models were compared. The result analysis shows the performance difference between the VADER model and TEXTBLOB model through analyzing the standardized scores and the Pearson correlation coefficient. And it was particularly noted that the VADER model is better at capturing emotional changes in game reviews, while the TEXTBLOB model may not fully represent the user’s emotional inclination towards various aspects related to the game. The results reveal significant differences in model performance, providing insights into their effectiveness for gaming comment analysis.
Keywords
NLP, Sentiment Analysis, Video Game, Rating, Comparison
[1]. Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X. (2019). Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access, 7, 51522–51532. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2909919
[2]. Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., Morency, L.-P. (2018a). Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph. In I. Gurevych Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2236–2246). Association for Computational Linguistics. https://doi.org/10.18653/v1/P18-1208
[3]. Chakraborty, S., Mobin, I., Roy, A., Khan, M. H. (2018). Rating Generation of Video Gamesusing Sentiment Analysis and Contextual Polarity from Microblog. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 157–161. https://doi.org/10.1109/CTEMS.2018.8769149
[4]. Xiao, Y., Qi, C., Leng, H. (2021a). Sentiment analysis of Amazon product reviews based on NLP. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 1218–1221. https://doi.org/10.1109/AEMCSE51986.2021.00249
[5]. Sirbu, D., Secui, A., Dascalu, M., Crossley, S. A., Ruseti, S., Trausan-Matu, S. (2016). Extracting Gamers’ Opinions from Reviews. 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 227–232. https://doi.org/10.1109/SYNASC.2016.044
[6]. Xu, H., Liu, B., Shu, L., Yu, P. S. (2019). BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (arXiv:1904.02232). arXiv. https://doi.org/10.48550/arXiv.1904.02232
[7]. Saju, B., Jose, S., Antony, A. (2020). Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs. 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), 186–193. https://doi.org/10.1109/ACCTHPA49271.2020.9213209
[8]. Raja, J. G. J. S., Juliet, S. (2023). Deep Learning-based Sentiment Analysis of Trip Advisor Reviews. 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 560–565. https://doi.org/10.1109/ICAAIC56838.2023.10140848
[9]. Yessenov, K., Misailovic, S. (2009). Sentiment Analysis of Movie. https://www.semanticscholar.org/paper/Sentiment-Analysis-of-Movie-Yessenov-Misailovic/cbad1c16d8270f1f1ecd542518ee933922bd647c
[10]. Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., Donaldson, L. (2013). Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online. Journal of Medical Internet Research, 15(11), e2721. https://doi.org/10.2196/jmir.2721
[11]. Gkillas, A., Simos, M. A., Makris, C. (2023). Popularity Inference Based on Semantic Sentiment Analysis of YouTube Video Comments. 2023 14th International Conference on Information, Intelligence, Systems Applications (IISA), 1–6. https://doi.org/10.1109/IISA59645.2023.10345963
[12]. Guzsvinecz, T., Szűcs, J. (2023). Length and sentiment analysis of reviews about top-level video game genres on the steam platform. Computers in Human Behavior, 149, 107955. https://doi.org/10.1016/j.chb.2023.107955
[13]. Guzsvinecz, T. (2023). Sentiment Analysis of Souls-like Role-Playing Video Game Reviews. Acta Polytechnica Hungarica, 20(5), 149–168. https://doi.org/10.12700/APH.20.5.2023.5.10
[14]. A, R., Kommarajula, R., Pranav, C. N., Prasanna, M. S. (2023). Analyzing the Voters’ View: Emotional Sentiment vs Long Run Development, of Casting Votes in Elections Using NLP. 2023 International Conference on Computational Intelligence, Networks and Security (ICCINS), 1–5. https://doi.org/10.1109/ICCINS58907.2023.10450030
[15]. Viggiato, M., Lin, D., Hindle, A., Bezemer, C.-P. (2022). What Causes Wrong Sentiment Classifications of Game Reviews? IEEE Transactions on Games, 14(3), 350–363. IEEE Transactions on Games. https://doi.org/10.1109/TG.2021.3072545
[16]. Thompson, J. J., Leung, B. H., Blair, M. R., Taboada, M. (2017). Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model. Knowledge-Based Systems, 137, 149–162. https://doi.org/10.1016/j.knosys.2017.09.022
[17]. McKeown, K., Agarwal, A., Biadsy, F. (2009). Contextual Phrase-Level Polarity Analysis using Lexical Affect Scoring and Syntactic N-grams. https://doi.org/10.7916/D88G8V15
[18]. Kumar, S., Kanshal, S., Sharma, V., Talwar, R. (2022). Sentiment Analysis on Amazon Alexa Reviews Using NLP Classification. 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 353–358. https://doi.org/10.1109/ICAC3N56670.2022.10074086
[19]. Urriza, I. M., Clariño, M. A. A. (2021). Aspect-Based Sentiment Analysis of User Created Game Reviews. 2021 24th Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA), 76–81. https://doi.org/10.1109/OCOCOSDA202152914.2021.9660559
[20]. Bunyamin, M. A. F., Pudjiantoro, T. H., Renaldi, F., Hadiana, A. I. (2022). Analyzing Sentiments on Indonesia’s New National Palace using The Combination of Naive Bayes and Sentiment Scoring. 2022 International Conference on Science and Technology (ICOSTECH), 1–6. https://doi.org/10.1109/ICOSTECH54296.2022.9829075
Cite this article
Yuan,L.;Ding,M.;Meng,F.;Tian,Y. (2025). Sentiment Analysis and Rating Video Game Dimensions via NLP. Applied and Computational Engineering,132,1-10.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).