Understanding Text Mining and Sentiment Analysis in Hotel Booking
(3 pages of text)
A management science professor had an unpleasant experience with a hotel she stayed at in New York City. Consequently, she wanted to figure out if hotel ratings were enough to recommend a hotel, or if customers’ text reviews could be used as more important and accurate indicators of customers’ hotel experiences. The exercise serves as an introduction to the topic of text analytics—specifically, sentiment analysis—and introduces the concept of text mining and the importance of dealing with unstructured datasets. Much of the exercise focuses on the method and rationale behind document indexing and the subsequent weighting of the indexed terms through term frequency–inverse document frequency. Textual data from customers’ hotel reviews are provided to apply the text mining techniques and to provide insight for a better decision-making process that would help the professor in her next hotel booking.
This exercise is suitable for an undergraduate-level course on management science. Students should have previous knowledge of basic statistical distributions, Microsoft Excel formulas, and R (a programming language for statistical computing). The exercise introduces sentiment analysis through the vector space model and promotes discussion of its potential applications, specifically for hotel booking. After completion of the exercise, students will be able to do the following:
- Understand the importance of data management.
- Recognize the difference between unstructured and structured data.
- Gain a foundational understanding of the field of text mining, particularly of sentiment analysis.
- Learn the steps needed to complete a vector space model and apply them accordingly.
- Understand term frequency–inverse document frequency and its application.
- Evaluate both the findings of their text analytics and their implications for strategic business decisions in the recommendation systems industry.
- Create a text mining package in R.
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