Analysis of Appointment Only Businesses in Yelp Dataset

Can correlations be found among the appointment only businesses that have five star ratings which could be applied to other businesses to increase their chance of being more highly rated? Conversely, can patterns be established among the lower rated appointment only businesses which other businesses could avoid to increase their chance of being more highly rated?

Businesses operating as appointment only skew abnormally high in customer ratings. This is compared to the much wider distribution, which skews closer to the midrange, of all other businesses when viewed as a whole. Both groups are plotted below.

The text from both tips and reviews for appointment only businesses was mined for both most frequent terms and most frequent trigrams (three word phrases). This was further broken down by star rating. The mining was done by subsetting out the text in question, building a corpus, removing punctuation, numbers, stopwords, whitespace and then stemming the result before ordering by frequency.Below is example output from frequent review terms with 5.0 star rating:

great, time, best, back, recommend, like, amazing, always, years, love, staff, feel, good, experience, friendly, professional, done, now, appointment, highly, well, went, care, day, job, definitely, every, comfortable, nice, right, since, service, better, awesome, happy, need, took, felt, wanted, anyone, super, wonderful, give, next, last, visit, thank, clean

On the low end of the star ratings people often describe having to wait, or having their time wasted. On the high end of the star ratings people are effusive about how great they were made to feel, often specifically noting a high level of comfort. We can extrapolate from this data that a major expectation customers have of appointment only businesses is that they do not waste their time, and that they are treated with a certain level of care and formality. Businesses dealing with customers could develop methods to identify recurring circumstances that waste time, and how to appropriately make their customers feel appreciated. The terms and phrases identified in this subset of the Yelp data provide a specific set of terms related to these concepts which could be used to quickly mine other datasets for specific reviews describing either positive or negative customer experiences. These experiences could be used to model new ways of approacing the business/customer interaction. It is very likely that this kind of coordinated approach to time efficiency and overall customer experience would result in an overall increase in positive sentiment towards a business.