Unveiling metaverse sentiments using machine learning approaches

被引:1
|
作者
Natarajan, Thamaraiselvan [1 ]
Pragha, P. [1 ]
Dhalmahapatra, Krantiraditya [2 ]
Raghavan, Deepak Ramanan Veera [3 ]
机构
[1] Natl Inst Technol Tiruchirappalli, Dept Management Studies, Tiruchirappalli, India
[2] IIM Shillong, Operat & Quantitat Tech, Shillong 793018, India
[3] CHRIST Deemed Univ, Sch Business & Management, Bengaluru, India
关键词
Qualitative research; Behavior; Neural nets; Information technology; ONLINE; HOSPITALITY; OPPORTUNITIES; CREDIBILITY; CHALLENGES; TOPICS; TRAVEL;
D O I
10.1108/K-11-2023-2268
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
PurposeThe metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one's intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.Design/methodology/approachThe current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.FindingsThe results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.Research limitations/implicationsAnalyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse's experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse's economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.Social implicationsIn terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.Originality/valueThe current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.
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页数:24
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