Kansei Engineering Evaluation on Game Design Using Customer Reviews
Pages 338 - 343
Abstract
Video games are digital entertainment with several design elements that form the basic framework for making them and become one of the influences on player enjoyment. One of the media to assess the influence of their enjoyment is the video game’s review text because it contains customers’ impressions and emotions when playing the game. Kansei engineering is a product design methodology that can connect customer emotions and a product, which in this context is game design elements. Conventional Kansei engineering stages are taking customer surveys, analyzing the survey results, and getting relational rules between products and Kansei. However, the first stage requires time, effort, and money. Emotions from customers can be extracted through their review texts to overcome those problems. In this paper, we propose a method to determine the appropriate Kansei word for each game design element and classify it using three machine learning algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The experiment was conducted using a dataset obtained from a review of a game on Steam. In this study, the SVM model obtained an accuracy above 80% and an average F1-score above 53%.
References
[1]
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-Generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 2623–2631. https://doi.org/10.1145/3292500.3330701
[2]
Kharis Alfian, Retno Indah Rokhmawati, and Fitra Abdurrachman Bachtiar. 2021. Pengembangan Desain Antarmuka Pengguna e-Learning Pemrograman menggunakan Metode Rekayasa Kansei. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 5, 10(2021), 4621–4630. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1002
[3]
Aseel Berglund, Erik Berglund, Fabio Siliberto, and Erik Prytz. 2017. Effects of reactive and strategic game mechanics in motion-based games, In 2017 IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH). 2017 IEEE 5th International Conference on Serious Games and Applications for Health, SeGAH 2017, 1–8. https://doi.org/10.1109/SeGAH.2017.7939275
[4]
Saiful Azman Hj Bidin and Anitawati Mohd Lokman. 2018. Enriching the comfortability emotion on website interface design using Kansei engineering approach. Advances in Intelligent Systems and Computing 739 (2018), 792–800. https://doi.org/10.1007/978-981-10-8612-0_82
[5]
S. Bird, E. Klein, and E. Loper. 2009. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media. https://books.google.co.id/books?id=KGIbfiiP1i4C
[6]
Man Ding, Lingying Zhao, Huining Pei, and Meijia Song. 2021. An XGBoost based evaluation methodology of product color emotion design. Journal of Advanced Mechanical Design, Systems and Manufacturing 15, 6(2021), JAMDSM0075–JAMDSM0075. https://doi.org/10.1299/JAMDSM.2021JAMDSM0075
[7]
Fu Guo, Wei Lin Liu, Yaqin Cao, Fan Tao Liu, and Mei Lin Li. 2016. Optimization Design of a Webpage Based on Kansei Engineering. Human Factors and Ergonomics In Manufacturing 26, 1(2016), 110–126. https://doi.org/10.1002/hfm.20617
[8]
Ana Hadiana, Rico Dahlan, Riyo Wardoyo, and Djohar Syamsi. 2017. Kansei analysis for transmedia storytelling products based on story and character. Proceedings - 2017 International Seminar on Application for Technology of Information and Communication: Empowering Technology for a Better Human Life, iSemantic 2017 2018-Janua (2017), 85–89. https://doi.org/10.1109/ISEMANTIC.2017.8251848
[9]
Zellig S. Harris. 1954. Distributional Structure. WORD 10, 2-3 (1954), 146–162. https://doi.org/10.1080/00437956.1954.11659520
[10]
Sho HASHIMOTO, Atsuhiro YAMADA, and Noriko NAGATA. 2019. A Quantification Method of Composite Impression of Products by Externalized Evaluation Words of the Appraisal Dictionary with Review Text Data. International Journal of Affective Engineering 18, 2(2019), 59–65. https://doi.org/10.5057/ijae.ijae-d-18-00020
[11]
Yu Hsiang Hsiao, Mu Chen Chen, and Wei Chien Liao. 2017. Logistics service design for cross-border E-commerce using Kansei engineering with text-mining-based online content analysis. Telematics and Informatics 34, 4 (2017), 284–302. https://doi.org/10.1016/j.tele.2016.08.002
[12]
Jianxin Jiao, Yiyang Zhang, and Martin Helander. 2006. A Kansei mining system for affective design. Expert Systems with Applications 30, 4 (2006), 658–673. https://doi.org/10.1016/j.eswa.2005.07.020
[13]
Tibor Kiss and Jan Strunk. 2006. Unsupervised multilingual sentence boundary detection. Computational Linguistics 32, 4 (2006), 485–525. https://doi.org/10.1162/coli.2006.32.4.485
[14]
Anitawati Mohd Lokman, Alfariz Mohd Mustafa, Mohd Firdauz Mohd Fathir, and Abd Rasid Abd Rahman. 2014. Avatar warrior: A Kansei analysis. In 2014 3rd International Conference on User Science and Engineering (i-USEr). IEEE, Shah Alam, Malaysia, 24–29. https://doi.org/10.1109/IUSER.2014.7002671
[15]
Mitsuo Nagamachi. 2004. Kansei Engineering. In Handbook of Human Factors and Ergonomics Methods. CRC Press, Boca Raton, FL, 698–702. https://books.google.co.id/books?id=RApSggShPc8C
[16]
Aniket Nagle, Peter Wolf, and Robert Riener. 2016. Towards a system of customized video game mechanics based on player personality: Relating the Big Five personality traits with difficulty adaptation in a first-person shooter game. Entertainment Computing 13 (2016), 10–24. https://doi.org/10.1016/j.entcom.2016.01.002
[17]
David Pinelle, Nelson Wong, and Tadeusz Stach. 2008. Heuristic Evaluation for Games: Usability Principles for Video Game Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Florence, Italy) (CHI ’08). Association for Computing Machinery, New York, NY, USA, 1453–1462. https://doi.org/10.1145/1357054.1357282
[18]
Heru Prastawa, Novie Susanto, and Octavia Gultom. 2018. Affective aspect analysis on online games from user’s point of view using Kansei engineering. Proceedings of the International Conference on Industrial Engineering and Operations Management 2018-March (2018), 2603–2610.
[19]
Huafeng Quan, Shaobo Li, Hongjing Wei, and Jianjun Hu. 2019. Personalized Product Evaluation Based on GRA-TOPSIS and Kansei Engineering. Symmetry 11, 7 (2019). https://doi.org/10.3390/sym11070867
[20]
Kittipong Sakornsathien, Sukree Sinthupinyo, and Pongpun Anuntavoranich. 2019. Application of Kansei engineering and data mining in developing an ingenious product co-design system. International Journal of Machine Learning and Computing 9, 1 (2019), 67–74. https://doi.org/10.18178/ijmlc.2019.9.1.767
[21]
Helmut Schmid. 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of the International Conference on New Methods in Language Processing. University of Manchester, Manchester, UK, 44–49. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.1139
[22]
Rosa Lilia Segundo Díaz, Gustavo Rovelo Ruiz, Miriam Bouzouita, and Karin Coninx. 2022. Building blocks for creating enjoyable games—A systematic literature review. International Journal of Human Computer Studies 159, February 2021(2022), 102758. https://doi.org/10.1016/j.ijhcs.2021.102758
[23]
Rafif Taqiuddin, Fitra A. Bachtiar, and Welly Purnomo. 2021. Opinion Spam Classification on Steam Review using Support Vector Machine with Lexicon-Based Features. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control 6, 4 (Nov. 2021). https://doi.org/10.22219/kinetik.v6i4.1323
[24]
Xinge Tong. 2021. Positioning game review as a crucial element of game user feedback in the ongoing development of independent video games. Computers in Human Behavior Reports 3, January (2021), 100077. https://doi.org/10.1016/j.chbr.2021.100077
[25]
Zachary O. Toups, Andruid Kerne, and William Hamilton. 2009. Game Design Principles for Engaging Cooperative Play: Core Mechanics and Interfaces for Non-Mimetic Simulation of Fire Emergency Response. In Proceedings of the 2009 ACM SIGGRAPH Symposium on Video Games (New Orleans, Louisiana) (Sandbox ’09). Association for Computing Machinery, New York, NY, USA, 71–78. https://doi.org/10.1145/1581073.1581085
[26]
Princeton University. 2010. About Wordnet. https://wordnet.princeton.edu/. Accessed: 2022-07-02.
[27]
W. M. Wang, Z. Li, Layne Liu, Z. G. Tian, and Eric Tsui. 2018. Mining of affective responses and affective intentions of products from unstructured text. Journal of Engineering Design 29, 7 (2018), 404–429. https://doi.org/10.1080/09544828.2018.1448054
[28]
W. M. Wang, Z. Li, Z. G. Tian, J. W. Wang, and M. N. Cheng. 2018. Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach. Engineering Applications of Artificial Intelligence 73, May(2018), 149–162. https://doi.org/10.1016/j.engappai.2018.05.005
[29]
W. M. Wang, J. W. Wang, Z. Li, Z. G. Tian, and Eric Tsui. 2019. Multiple affective attribute classification of online customer product reviews: A heuristic deep learning method for supporting Kansei engineering. Engineering Applications of Artificial Intelligence 85, June(2019), 33–45. https://doi.org/10.1016/j.engappai.2019.05.015
[30]
Xiaohui Wang, Dion Hoe Lian Goh, Ee Peng Lim, and Adrian Wei Liang Vu. 2015. Aesthetic experience and acceptance of human computation games. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9469 (2015), 264–273. https://doi.org/10.1007/978-3-319-27974-9_28
[31]
Yaxue Zuo and Zhenya Wang. 2020. Subjective product evaluation system based on kansei engineering and analytic hierarchy process. Symmetry 12, 8 (2020), 1–25. https://doi.org/10.3390/sym12081340
Index Terms
- Kansei Engineering Evaluation on Game Design Using Customer Reviews
Recommendations
A Kansei Engineering Evaluation of the Emotional Appeal of Product Information on E-Commerce Product Pages
CHI Greece 2021: CHI Greece 2021: 1st International Conference of the ACM Greek SIGCHI ChapterThe study proposes a Kansei Engineering methodology for the evaluation of customer impressions formed by the product information presenting a food supplement on e-commerce product pages. The study examined the design elements currently used for ...
Classification of Multiple Affective Attributes of Customer Reviews: Using Classical Machine Learning and Deep Learning
CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application EngineeringAffective1 engineering is a methodology of designing products by collecting customer affective needs and translating them into product designs. It usually begins with questionnaire surveys to collect customer affective demands and responses. However, ...
Kansei Engineering: Emotion Exploration in Blogs
ICBAKE '13: Proceedings of the 2013 International Conference on Biometrics and Kansei EngineeringThe Kansei design method is gaining wide acceptance all around the world. Researchers have begun to address emotional elements in various domain of research to ensure product acceptability and feasibility. Kansei is known for its uniqueness as it varies ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2022
398 pages
ISBN:9781450397117
DOI:10.1145/3568231
Copyright © 2022 ACM.
© 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 January 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
SIET '22
SIET '22: 7th International Conference on Sustainable Information Engineering and Technology 2022
November 22 - 23, 2022
Malang, Indonesia
Acceptance Rates
Overall Acceptance Rate 45 of 57 submissions, 79%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 89Total Downloads
- Downloads (Last 12 months)42
- Downloads (Last 6 weeks)7
Reflects downloads up to 22 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format