Blog on Text Analytics - Provalis Research 2024年11月27日
Bibliometrics, Using Text Mining to Review Twitter Usage in Tourism
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本文介绍了一篇关于社交媒体,特别是Twitter在旅游业应用的研究论文。该研究利用文本挖掘技术对相关文献进行分析,揭示了Twitter在旅游决策、酒店品牌塑造、电子口碑传播和预订意愿等方面的影响。研究发现,Twitter的使用可以帮助旅游企业制定更精准的营销策略,提供个性化的服务,从而获得竞争优势。此外,该研究也强调了文本分析在旅游研究中的重要作用,为相关领域的研究提供了新的思路和方法。

🤔该研究利用文本挖掘技术分析了Twitter在旅游业中的应用,包括旅游决策、酒店品牌塑造、电子口碑传播和预订意愿等方面。

📊研究通过对46篇相关论文的摘要进行文本挖掘,提取关键词和短语,并利用关联分析和聚类分析识别主题。

🔎研究发现,Twitter的使用与游客的决策、酒店品牌、电子口碑传播和预订意愿等密切相关,为旅游企业提供了制定营销策略的新思路。

💡研究强调了文本分析在旅游研究中的重要性,为相关研究提供了新的方法和思路。

🎯个性化服务被认为是旅游企业获得竞争优势和扩大市场份额的关键因素。

 

 

Social media platforms are becoming ubiquitous in the tourism industry around the world. Travelers are using social media to write reviews, plan travel, connect with others and communicate with providers. Likewise, airlines, hotels, restaurants and tour companies, are using social media to market and promote their products, communicate with their clients and as a tactic of crisis communications. These are just a few examples. As a result of this activity academics are more and more studying the impact of social media on tourism. In their paper “Twitter Usage in Tourism: Literature Review” Business Systems Research, Vol. 10 No. 1, pp. 102-119 Curlin, T., Jakovic,B., Miloloza I. (2019) use text mining to perform a bibliometric analysis to identify significant authors, journals and institutions engaged in the research-oriented  utilization of Twitter in tourism and to extract and identify the words, phrases, topics and themes in this research.

After completing a systematic literature review using the phrase Twitter and Touris* and then eliminating papers not directly related to the research topic the authors compiled a list of 46 papers. They then used WordStat 8.0.9 to perform text mining on the abstracts of all the papers to extract words and phrases. Through the use of link analysis and the software’s proximity plot feature they identified the proximity between words and phrases and performed cluster analysis to identify common themes.

The cluster analysis identified seven topics based on the phrases extracted from the papers related to Twitter and tourism. Among their conclusions, the authors stated that, “the cluster analysis singled out the trends, methods and gaps in the literature. It specified six topics where an assorted number of sources confirmed the relationship of Twitter posts and tourist’s decision making, hotel branding, e-WoM and booking intentions so hotels and tourist destinations can create business and marketing strategies and more distinguished and personalized supply. Personalized supply is singled out as a critical factor in gaining competitive advantage and marketing spread.”

The paper shows the value of text analytics and text mining in performing bibliometric research. If you would like to read more about bibliometrics and scientometrics with Wordstat and QDA Miner you can access our white paper. You can read the full paper highlighted in this Blog here

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社交媒体 旅游业 文本挖掘 Twitter 个性化服务
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