Customers’ metaverse service encounter perceptions: sentiment analysis and topic modeling

ABSTRACT

Using machine learning, we examined customers’ opinions about the metaverse in the hospitality industry (encompassing hotels, restaurant, gaming, virtual events, tours and travel). A total of 8,855 tweets were collected from Twitter (now called X), and machine learning algorithms such as sentiment analysis and topic modeling were performed using Python libraries to capture the important topics related to metaverse applications. Nearly two thirds of the collected tweets (60.9%) contained a mostly positive general sentiment toward the use of the metaverse. Six important topics emerged from the topic modeling: gaming, virtual events, virtual sightseeing, travel, business and blockchain. Despite numerous studies on the proper integration of the metaverse, VR and AR, to the best of our knowledge, this is one of the first studies conducted to determine the customer experience of the metaverse in the hospitality industry using social media data.

摘要

通过机器学习,我们研究了客户对酒店业元宇宙的看法(包括酒店、餐厅、游戏、虚拟活动、旅游和旅行). 从Twitter(现在称为X)收集了总共8855条推文,并使用Python库执行了情感分析和主题建模等机器学习算法,以捕获与元宇宙应用程序相关的重要主题. 在收集到的推文中,近三分之二(60.9%)对元宇宙的使用持积极态度. 主题建模中出现了六个重要主题:游戏、虚拟活动、虚拟观光、旅游、商业和区块链. 尽管对元宇宙、VR和AR的正确整合进行了大量研究,但据我们所知,这是首批使用社交媒体数据确定酒店业元宇宙客户体验的研究之一.

KEYWORDS:

• Metaverse
• hospitality
• hotels
• natural language processing
• sentiment analysis
• topic modelling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Compliance with ethical standards

Informed consent has been obtained while collecting data. Thus, ethical standards are complied.