From the positive word cloud, it can be observed that this location does a great job at being kind and friendly, as those are the largest words in the cloud. However, knowledge gained from the negative word cloud may be more insightful. From here, it can be observed that some of the larger words include “wait” and “stopped”.
If people are using the word “wait” in their reviews, it may indicate that many customers at this location have to wait a significantly longer time to get their food. The same inference cannot be made for the word “stopped” however. So in this case, it would be helpful to use the word cloud as a filter to retrieve the reviews that have the word “stopped” in them to see what problem exists. It could be that employees are frequently sidetracked, distracted, or stopped helping customers.
Customer sentiment analysis data can not only help understand these reviews, but also aid in gaining insights into revenue. Take for example the following visualization, which factors in revenue and net promoter scores: