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.

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.