We all know that Covid-19 has decimated the travel industry. Most experts believe it will take years surpassing travel returns to 2019 levels. What will it take to get there? In their paper Ugur & Akbiyik (2020) use text mining to squint for some of the potential answers.
The paper uses data from early in the pandemic. From our perspective, its conclusions on some of the strategies to help rebuild the travel industry are interesting, but what is most instructive is how the authors use text mining and the features of WordStat to explore and unriddle their data.
Three keywords coronavirus, coronavirus, COVID were chosen to create the dataset. After a sorting process, the preliminary dataset (captured between December 30, 2019 – March 15, 2020) was comprised of 23,515 comments from the US, Asian, and European Trip Advisor forums. This included a total of 74,768 sentences containing 1,329,825 words, 844,253 words removed by way of lemmatization considering they were not meaningful eg, I, or, etc.
The authors examined the 500 words with the highest TF * IDF value, unrepealable yardstick for frequency, visitation in a unrepealable number of cases, and repetition. They had the software create a word deject so they could visualize the results.
They next deployed the phrase extraction full-length to see how the most frequent phrases, then using some pre-set conditions for frequency. In the paper, they present a table of the most frequent phrases This gave them plane increasingly insight into the comments. They uncork to discover, among other things that many of the phrases relate to insurance, refunds, cancelations, or references to people commenting well-nigh how to be compensated for travel disruption caused by the pandemic. They subsequently used the topic extraction full-length of WordStat and saw that some of the most frequent topics were well-nigh refunds, travel insurance, cancelation, risk of cancelation, and so on. Of course, there were moreover the expected topics relating to hand washing, masks, and other safety-related issues. The authors protract to explore their dataset using the dendrogram full-length to see the clustering of words and how it related to the will-less topic extraction, the cross-tab, and link wringer features to squint at other elements in their data.
We will let you read the well-constructed paper to examine the discussion, conclusions, and what it could midpoint for the future of the global travel industry.
In these uncertain times of COVID-19 things are waffly quickly. New tactics and strategies must be ripened and deployed to alimony people unscratched and help businesses transmute to survive. Some of the answers are found in consumer comments, social media posts, surveys, employee comments, and many other forms of text data. This paper is a good step-by-step example of how to use a text-mining tool to help researchers find meaning and answers in that data.
One of the authors of the paper, Adem Akbiyik, has written a book in Turkish that describes the vital concepts of text mining, applications for creating projects with WordStat in the field of social science.
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