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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (1): 49-62    DOI: 10.11925/infotech.2096-3467.2022.0371
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The Ideal and Reality of Metaverse: User Perception of VR Products Based on Review Mining
Cao Zhe1,Guo Huilan1,Wu Jiang1,2(),Hu Zhongyi1,2
1School of Information Management, Wuhan University, Wuhan 430072, China
2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper investigates the gap between users’ perception of VR products and the ideal technical requirements of the metaverse, aiming to support the latter’s optimization. [Methods] First, we retrieved 36 720 user reviews of 64 VR products sold by JD.com. Then, we used the LDA topic model and BERT language model to construct indicators of attention and affection. Third, we quantitatively analyzed the users’ perception of VR products(technology). Finally, we compared these objective attributes of VR products and the technical requirements of the metaverse. [Results] We extracted five perceived attributes (function, quality control, use feeling, marketing and audio-visual experience) from the reviews. The audio-visual experience has the highest attention and affection while marketing is the lowest. The function, use feeling and audio-visual experience have eight progressive or regressive manifestations in the four dimensions of technical requirements in the metaverse (immersion experience, accessibility, interoperability and scalability). The eight manifestations are high immersion, sensory imbalance, multiple connections, time and space constraints, multiplayer interaction, mobile obstacles, multi-functional design and equipment problems. [Limitations] The diversity and balance of samples need to be improved, and more research should be conducted on other types of metaverse equipment. [Conclusions] The existing VR products can meet the technical requirements of the metaverse in immersion experience, but there is still a long way to go to achieve accessibility, interoperability and scalability.

Key wordsMetaverse      VR Products      User Perception      Topic Clustering      Sentiment Classification     
Received: 20 April 2022      Published: 16 February 2023
ZTFLH:  G353  
Fund:Key Projects of Philosophy and Social Sciences Research, Ministry of Education(20JZD024);National Natural Science Foundation of China(71874131)
Corresponding Authors: Wu Jiang,ORCID:0000-0001-5153-5871,E-mail:jiangw@whu.edu.cn。   

Cite this article:

Cao Zhe, Guo Huilan, Wu Jiang, Hu Zhongyi. The Ideal and Reality of Metaverse: User Perception of VR Products Based on Review Mining. Data Analysis and Knowledge Discovery, 2023, 7(1): 49-62.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0371     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I1/49

Research Ideas
品牌 类别 连接方式 双眼分辨率 视场角(度)
Pico 24 37.5% VR头显 19 29.7% 蓝牙 17 26.6% 4 896×2 448 2 3.1% 360 1 1.6%
华为(HUAWEI) 10 15.6% AR眼镜 1 1.6% USB 27 42.2% 4 320×2 160 7 10.9% 120 4 6.3%
NOLO 10 15.6% VR一体机 44 68.8% USB-C 23 35.9% 3 840×2 160 6 9.4% 110 12 18.8%
HTC VIVE 9 14.1% 功能 网线 3 4.7% 3 664×1 920 20 31.3% 101 6 9.4%
HTC 6 9.4% WiFi 40 62.5% 3 200×1 600 4 6.3% 100 4 6.3%
爱奇艺VR 5 7.8% 玩游戏 16 25.0% 其它 21 32.8% 2 880×1 600 9 14.1% 98 24 37.5%
适用场景 看电影 4 6.3% 2 560×1 440 2 3.1% 96 2 3.1%
近视可用 7 10.9% 1 440×1 600 1 1.6% 93 4 6.3%
卧室/客厅/浴室/书房 63 98.4% 瞳距调节 6 9.4% 其它 12 18.8% 90 6 9.4%
地铁/动车/高铁/飞机 1 1.6% 未标识 31 48.4% 延迟率 未标识 1 1.6% 0 1 1.6%
适用人群 电池 屏幕材质
小于等于20ms 50 78.1%
游戏达人 59 92.2% 无电池 21 32.8% 小于等于40ms 5 7.8% OLED 11 17.2% 无屏幕 1 1.6%
追剧达人 5 7.8% 内置电池 43 67.2% 小于等于60ms 9 14.1% LCD 32 50.0% 未标识 20 31.3%
Numbers and Proportions of Different Categories of Objective Attribute of VR Products
Technical Route
Sentiment Classification Model
情感倾向 含义
-1 负向情感(不满意、消极)
0 中性情感(未显露出情感倾向)
1 正向情感(满意、积极)
Sentiment Classification Rules
品牌 主题 共同关键词 特有关键词
全部 使用感受 操作 简单 舒服 清晰 上手 做工 舒适 佩戴 眼镜 外观
功能 游戏 体验 手柄 pico 串流 支持 电脑 设备 定位 steam
品控 客服 体验 打卡 游戏 活动 很快 下单 包装 物流 第二天 耐心
视听体验 体验 清晰 电影院 游戏 电影 好玩 玩游戏 视频 第一次 身临其境
HTC 功能 游戏 体验 安装 手柄 定位 操作 简单 定位器 清晰 舒服
品控 体验 客服 包装 很快 质量 htc 好评 值得 价格 公司
HTC VIVE 品控 体验 客服 包装 游戏 配件 技术人员 解决 商家 无线 服务态度
使用感受 操作 舒服 简单 安装 技术 清晰 想象 科技 质量 htc
华为 功能 游戏 体验 手机 电影 华为 眼镜 视频 连接 3d 资源
品控 体验 客服 华为 包装 价格 物流 值得 支持 很快 下单
使用感受 操作 简单 舒服 清晰 上手 近视 做工 华为 舒适 科技
爱奇艺VR 营销 游戏 体验 电影 好玩 打卡 活动 爱奇艺 奇遇 第一次 会员
使用感受 操作 简单 舒服 手柄 佩戴 上手 舒适 游戏 外观 做工
视听体验 体验 电影院 清晰 电影 影院 震撼 值得 3d 质量 爱奇艺
NOLO 视听体验 体验 清晰 电影院 电影 游戏 眼镜 华为 手机 视频 玩游戏
品控 体验 客服 游戏 性价比 第一次 steam 值得 孩子 手柄 推荐
使用感受 操作 简单 舒服 清晰 质量 上手 做工 舒适 外观 包装
Pico 营销 游戏 打卡 活动 体验 好玩 电影 运动 入手 180 值得
功能 体验 游戏 pico 串流 neo3 支持 国产 手柄 视频 电影
品控 客服 体验 很快 下单 迫不及待 第二天 游戏 包装 物流 耐心
使用感受 操作 简单 舒服 清晰 上手 游戏 眼镜 手柄 佩戴 舒适
Results of Topic Clustering
Numbers of Reviews Among Six Brands
属性 准确率 召回率 F1值
功能 0.806 0.772 0.780
品控 0.884 0.869 0.871
使用感受 0.815 0.805 0.788
营销 0.832 0.851 0.820
视听体验 0.827 0.789 0.783
The Results of Five Perceived Attributes
属性 正向评论数 中性评论数 负向评论数 评论数
占比(%)
功能 12 400 664 2 078 41.3
品控 14 594 410 859 43.2
使用感受 15 387 874 1 106 47.3
营销 2 056 2 632 90 13.1
视听体验 19 014 322 697 54.6
Numbers of Reviews with Positive, Impartial and Negative Emotions of Five Perceived Attributes
Attention and Affection of VR Products on Five Perceived Attributes
Attention and Affection of VR Products on Five Perceived Attributes Among Six Brands
Technical Requirements of Metaverse and Progressive and Regressive Performance of Perceived Attributes
[1] 吴江, 曹喆, 陈佩, 等. 元宇宙视域下的用户信息行为:框架与展望[J]. 信息资源管理学报, 2022, 12(1): 4-20.
[1] ( Wu Jiang, Cao Zhe, Chen Pei, et al. Users’ Information Behavior from the Perspective of Metaverse: Framework and Prospect[J]. Journal of Information Resources Management, 2022, 12(1): 4-20.)
[2] 王文喜, 周芳, 万月亮, 等. 元宇宙技术综述[J]. 工程科学学报, 2022, 44(4): 744-756.
[2] ( Wang Wenxi, Zhou Fang, Wan Yueliang, et al. A Survey of Metaverse Technology[J]. Chinese Journal of Engineering, 2022, 44(4): 744-756.)
[3] 龚才春. 中国元宇宙白皮书[R]. 北京: 北京信息产业协会, 2022.
[3] ( Gong Caichun. China Metaverse White Paper[R]. Beijing: Beijing Information Industry Association, 2022.)
[4] Wright M, Ekeus H, Coyne R, et al. Augmented Duality: Overlapping a Metaverse with the Real World[C]// Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology. 2008: 263-266.
[5] 方凌智, 沈煌南. 技术和文明的变迁——元宇宙的概念研究[J]. 产业经济评论, 2022(1): 5-19.
[5] ( Fang Lingzhi, Shen Huangnan. Conceptualizing Metaverse: A Perspective from Technology and Civilization[J]. Review of Industrial Economics, 2022(1): 5-19.)
[6] 聂辉华, 李靖. 元宇宙的秩序:一个不完全契约理论的视角[J]. 产业经济评论, 2022(2): 186-198.
[6] ( Nie Huihua, Li Jing. The Order of the Metaverse: A Perspective of Incomplete Contract Theory[J]. Review of Industrial Economics, 2022(2): 186-198.)
[7] 张夏恒, 李想. 国外元宇宙领域研究现状、热点及启示[J]. 产业经济评论, 2022(2): 199-214.
[7] ( Zhang Xiaheng, Li Xiang. Research Status, Hot Spots and Enlightenment in the Field of Metaverse Abroad[J]. Review of Industrial Economics, 2022(2): 199-214.)
[8] 刘革平, 王星, 高楠, 等. 从虚拟现实到元宇宙:在线教育的新方向[J]. 现代远程教育研究, 2021, 33(6): 12-22.
[8] ( Liu Geping, Wang Xing, Gao Nan, et al. From Virtual Reality to Metaverse: A New Direction of Online Education[J]. Modern Distance Education Research, 2021, 33(6): 12-22.)
[9] 车致新. 电影、 游戏、元宇宙:交互界面的媒介谱系[J]. 当代电影, 2021(12): 27-32.
[9] ( Che Zhixin. Film, Game, Metaverse: Media Spectrum of Interactive Interfaces[J]. Contemporary Cinema, 2021(12): 27-32.)
[10] 陆岷峰. 关于当前我国元宇宙发展及在商业银行的应用战略研究[J]. 当代经济管理, 2022, 44(6): 77-86.
[10] ( Lu Minfeng. On the Current Development of China’s Meta-Universe and Research on Its Application Strategy in Commercial Banks[J]. Contemporary Economic Management, 2022, 44(6): 77-86.)
[11] 高奇琦, 隋晓周. 元宇宙的政治社会风险及其防治[J]. 新疆师范大学学报(哲学社会科学版), 2022, 43(4): 104-115.
[11] Gao Qiqi, Sui Xiaozhou. The Political and Social Risk of Metaverse and Its Prevention[J]. Journal of Xinjiang Normal University(Philosophy and Social Sciences), 2022, 43(4): 104-115.)
[12] 冯立杰, 关柯楠, 王金凤. 基于在线评论考虑潜在用户需求的产品创新方案识别研究[J]. 情报理论与实践, 2022, 45(6): 129-137.
[12] ( Feng Lijie, Guan Kenan, Wang Jinfeng. Research on Product Innovation Scheme Identification Considering Potential User Needs Based on Online Comments[J]. Information Studies: Theory & Application, 2022, 45(6): 129-137.)
[13] Davis A, Murphy J, Owens D, et al. Avatars, People, and Virtual Worlds: Foundations for Research in Metaverses[J]. Journal of the Association for Information Systems, 2009, 10(2): 90-117.
doi: 10.17705/1jais.00183
[14] Matsubara M, Oguchi M. Evaluation of Metaverse Server in a Widely-Distributed Environment[C]// Proceedings of the OTM 2010 Workshops. 2010: 307-316.
[15] Sweeney T. Foundational Principles & Technologies for the Metaverse[C]// Proceedings of the ACM SIGGRAPH 2019 Talks. 2019:Article No.38.
[16] Egliston B, Carter M. Critical Questions for Facebook’s Virtual Reality: Data, Power and the Metaverse[J]. Internet Policy Review. DOI: 10.14763/2021.4.1610.
doi: 10.14763/2021.4.1610
[17] Eno J, Gauch S, Thompson C. Searching for the Metaverse[C]// Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology. 2009: 223-226.
[18] Cammack R G. Location-Based Service Use: A Metaverse Investigation[J]. Journal of Location Based Services, 2010, 4(1): 53-65.
doi: 10.1080/17489721003742827
[19] Chewslack-Postava E, Azim T, Mistree B F T, et al. A Scalable Server for 3D Metaverses[C]// Proceedings of the 2012 USENIX Annual Technical Conference. 2012: 209-222.
[20] Kim T, Kim S. Digital Transformation, Business Model and Metaverse[J]. Journal of Digital Convergence, 2021, 19(11): 215-224.
[21] Jung S H, Jeon I O. A Study on the Components of the Metaverse Ecosystem[J]. Journal of Digital Convergence, 2022, 20(2): 163-174.
[22] Dionisio J D N, Burns W G, Gilbert R. 3D Virtual Worlds and the Metaverse[J]. ACM Computing Surveys, 2013, 45(3): 1-38.
[23] Zhang L J. MRA: Metaverse Reference Architecture[C]// Proceedings of the International Conference on Internet of Things. 2021: 102-120.
[24] Baía Reis A, Ashmore M. From Video Streaming to Virtual Reality Worlds: An Academic, Reflective, and Creative Study on Live Theatre and Performance in the Metaverse[J]. International Journal of Performance Arts and Digital Media, 2022, 18(1): 7-28.
doi: 10.1080/14794713.2021.2024398
[25] Han D I D, Bergs Y, Moorhouse N. Virtual Reality Consumer Experience Escapes: Preparing for the Metaverse[J]. Virtual Reality, 2022, 26(4): 1443-1458.
doi: 10.1007/s10055-022-00641-7
[26] 杭云, 苏宝华. 虚拟现实与沉浸式传播的形成[J]. 现代传播(中国传媒大学学报), 2007, 29(6): 21-24.
[26] Hang Yun, Su Baohua. Virtual Reality and the Formation of Immersive Communication[J]. Modern Communication(Journal of Communication University of China), 2007, 29(6): 21-24.)
[27] Carreño L V G, Winbladh K. Analysis of User Comments: An Approach for Software Requirements Evolution[C]// Proceedings of the 35th International Conference on Software Engineering. IEEE, 2013: 582-591.
[28] 涂海丽, 唐晓波, 谢力. 基于在线评论的用户需求挖掘模型研究[J]. 情报学报, 2015, 34(10): 1088-1097.
[28] ( Tu Haili, Tang Xiaobo, Xie Li. Research on User Needs Mining Model Based on Online Reviews[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(10): 1088-1097.)
[29] 孙冰, 沈瑞. 基于在线评论的产品需求偏好判别与客户细分——以智能手机为例[J/OL]. 中国管理科学. [2022-04-18]. DOI:10.16381/j.cnki.issn1003-207x.2020.0164.
doi: 10.16381/j.cnki.issn1003-207x.2020.0164
[29] ( Sun Bing, Shen Rui. Online Reviews for Product Demand Preference Discrimination and Customer Segmentation: A Case Study of the Smart Phone Data[J/OL]. Chinese Journal of Management Science. [2022-04-18]. DOI:10.16381/j.cnki.issn1003-207x.2020.0164.)
doi: 10.16381/j.cnki.issn1003-207x.2020.0164
[30] Pang B, Lee L, Vaithyanathan S. Thumbs Up? Sentiment Classification Using Machine Learning Techniques[C]// Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing. 2002: 79-86.
[31] Wilson T, Wiebe J, Hoffmann P. Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis[J]. Computational Linguistics, 2009, 35(3): 399-433.
doi: 10.1162/coli.08-012-R1-06-90
[32] Karamibekr M, Ghorbani A A. Sentence Subjectivity Analysis in Social Domains[C]// Proceedings of the 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies. 2013: 268-275.
[33] 沈超, 王安宁, 方钊, 等. 基于在线评论数据的产品需求趋势挖掘[J]. 中国管理科学, 2021, 29(5): 211-220.
[33] ( Shen Chao, Wang Anning, Fang Zhao, et al. Trend Mining of Product Requirements from Online Reviews[J]. Chinese Journal of Management Science, 2021, 29(5): 211-220.)
[34] 王雪, 董庆兴, 张斌. 面向在线评论的用户需求分析框架与实证研究——基于KANO模型[J]. 情报理论与实践, 2022, 45(2): 160-167.
[34] ( Wang Xue, Dong Qingxing, Zhang Bin. Analytical Framework and Empirical Study of User Needs for Online Reviews Based on KANO Model[J]. Information Studies: Theory & Application, 2022, 45(2): 160-167.)
[35] 吴江, 刘涛, 刘洋. 在线社区用户画像及自我呈现主题挖掘——以网易云音乐社区为例[J]. 数据分析与知识发现, 2022, 6(7): 56-69.
[35] ( Wu Jiang, Liu Tao, Liu Yang. Mining Online User Profiles and Self-Presentations: Case Study of NetEase Music Community[J]. Data Analysis and Knowledge Discovery, 2022, 6(7): 56-69.)
[36] 张婧怡. 基于扎根理论的图书馆形象用户感知研究——来自大众点评网用户评论[J]. 图书馆工作与研究, 2021(S1): 5-9.
[36] ( Zhang Jingyi. Research on User Perception of Library Image Based on Grounded Theory—A Case Study of User Comments from Dianping.com[J]. Library Work and Study, 2021(S1): 5-9.)
[37] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[38] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[39] 余同瑞, 金冉, 韩晓臻, 等. 自然语言处理预训练模型的研究综述[J]. 计算机工程与应用, 2020, 56(23): 12-22.
doi: 10.3778/j.issn.1002-8331.2006-0040
[39] ( Yu Tongrui, Jin Ran, Han Xiaozhen, et al. Review of Pre-Training Models for Natural Language Processing[J]. Computer Engineering and Applications, 2020, 56(23): 12-22.)
doi: 10.3778/j.issn.1002-8331.2006-0040
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[2] Hu Jiming, Zheng Xiang. Abstracting Interactive Contents from New Media for Government Affairs Based on Topic Clustering[J]. 数据分析与知识发现, 2022, 6(6): 95-104.
[3] Zhang Wei, Wang Hao, Chen Yuetong, Fan Tao, Deng Sanhong. Identifying Metaphors and Association of Chinese Idioms with Transfer Learning and Text Augmentation[J]. 数据分析与知识发现, 2022, 6(2/3): 167-183.
[4] Feng Xiaodong, Hui Kangxin. Topic Clustering for Social Media Texts with Heterogeneous Graph Neural Networks[J]. 数据分析与知识发现, 2022, 6(10): 9-19.
[5] Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion[J]. 数据分析与知识发现, 2021, 5(6): 103-114.
[6] Zhou Wenyuan, Wang Mingyang, Jing Yu. Automatic Classification of Citation Sentiment and Purposes with AttentionSBGMC Model[J]. 数据分析与知识发现, 2021, 5(12): 48-59.
[7] Qi Ruihua,Jian Yue,Guo Xu,Guan Jinghua,Yang Mingxin. Sentiment Analysis of Cross-Domain Product Reviews Based on Feature Fusion and Attention Mechanism[J]. 数据分析与知识发现, 2020, 4(12): 85-94.
[8] Qingqing Zhang,Xingshi He,Huimin Wang,Shengjun Meng. Text Sentiment Classification Based on Deep Belief Network[J]. 数据分析与知识发现, 2019, 3(4): 71-79.
[9] Qiang Lu,Zhenfang Zhu,Fuyong Xu,Qiangqiang Guo. Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules[J]. 数据分析与知识发现, 2019, 3(11): 99-107.
[10] Hui Li,Yaqing Chai. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2019, 3(1): 95-103.
[11] Wang Xiufang,Sheng Shu,Lu Yan. Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity[J]. 数据分析与知识发现, 2018, 2(6): 37-47.
[12] Wang Shuyi,Liao Huatao,Wu Chake. Mining News on Competitors with Sentiment Classification[J]. 数据分析与知识发现, 2018, 2(3): 70-78.
[13] Zhang Qingqing,Liu Xilin. Classifying Sentiments Based on BPSO Random Subspace[J]. 数据分析与知识发现, 2017, 1(5): 71-81.
[14] Wang Xiaoyun,Yuan Yuan,Shi Lingling. Predicting Opening Weekend Box Office Prediction Based on Microblog[J]. 现代图书情报技术, 2016, 32(4): 31-39.
[15] Guo Shunli,Zhang Xiangxian. Building Sentiment Analysis Dictionary for Chinese Book Reviews[J]. 现代图书情报技术, 2016, 32(2): 67-74.
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