Unveiling metaverse sentiments using machine learning approaches

Title

Unveiling metaverse sentiments using machine learning approaches

Creator

Natarajan T.; Pragha P.; Dhalmahapatra K.; Veera Raghavan D.R.

Description

Purpose: The 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 ones 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/approach: The 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. Findings: The 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/implications: Analyzing 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 metaverses 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 metaverses 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 implications: In 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/value: The 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. 2024, Emerald Publishing Limited.

Source

Kybernetes

Date

2024-01-01

Publisher

Emerald Publishing

Subject

Behavior; Information technology; Neural nets; Qualitative research

Coverage

Natarajan T., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Dhalmahapatra K., Operations and Quantitative Techniques, IIM Shillong, Shillong, India; Veera Raghavan D.R., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 0368492X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1108/K-11-2023-2268

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188192750&doi=10.1108%2fK-11-2023-2268&partnerID=40&md5=214cdd4eeb7dcd452f9391b2d9870084