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arXiv · 2403.01095

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

Kadhim Hayawi·Sakib Shahriar·Mohamed Adel Serhani·Eiman Alothali
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Abstract

Metaverse has emerged as a novel technology with the objective to merge the physical world into the virtual world. This technology has seen a lot of interest and investment in recent times from prominent organizations including Facebook which has changed its company name to Meta with the goal of being the leader in developing this technology. Although people in general are excited about the prospects of metaverse due to potential use cases such as virtual meetings and virtual learning environments, there are also concerns due to potential negative consequences. For instance, people are concerned about their data privacy as well as spending a lot of their time on the metaverse leading to negative impacts in real life. Therefore, this research aims to further investigate the public sentiments regarding metaverse on social media. A total of 86565 metaverse-related tweets were used to perform lexicon-based sentiment analysis. Furthermore, various machine and deep learning models with various text features were utilized to predict the sentiment class. The BERT transformer model was demonstrated to be the best at predicting the sentiment categories with 92.6% accuracy and 0.91 F-measure on the test dataset. Finally, the implications and future research directions were also discussed.

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Kadhim Hayawi, Sakib Shahriar, Mohamed Adel Serhani, Eiman Alothali. 2024-03-02. Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse. https://arxiv.org/abs/2403.01095

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