Yelp Wannabe

Details

Yelp Wannabe was the first personal project I did outside of school and outside of a hackathon. I wanted to see if it was possible to rank businesses, specifically restaurants, based on their written reviews. I was curious to see if they matched up with Yelp’s top 10 ranking that weighs the star score, location, and cost.

I do not remember my motivation for using Python’s Beautiful Soup module to manually scrape the website food reviews but it was probably due to the fact that I didn’t have access to the Yelp API at the time. Or I was just an idiot- which probably the actual reason. Anyways, for every businesses (up to a total of 200, I think), I parsed all their reviews and outputed a ranking based on the written review. My first attempt was the simplest- map every word to a sentiment score from a massive sentiment dicitonary I found online.

This naiive approach actually didn’t do that badly but it got easly confused by words of neutral or nuanced connotation. For example, words such as “sloppy” could indicate that a restaurant was messy or unclean but if the business specialized in sloppy Joe’s they would get unfairly penalized. Basically, the naiive method sucks for obvious reasons- it didn’t look at context, parts of speech, or structure. Pretty much, it didn’t perform sentiment analysis at all.

At this point, I was really lazy so I decided to use Python NLTK for sentiment analysis. Python NLTK utilizes Twitter sentiment and movie reviews in order to train a Bayes’ classifier. At the time, I didn’t know anything about ML so it boiled down to me reading learning a lot of new concepts. Python NLTK turned out to be quite powerful out of the box and pretty much negated all the effort I put into find and stiching together massive sentiment dictionaries. At the end of the day, Yelp Wannabe actually was able to match 6 out of 10 Yelp’s top 10 list. Though this is not that impressive, it was pretty fun to learn about classifiers and scraping in the process.