The files I used are “Business.json” which includes a “Business ID” and average “Stars” for each ID, and “Review.json” which includes the “Business ID”, “Stars”, “Text” from each user.įor the first analysis, I translated each review text into English and then calculated the polarity of each review from a user using the TextBlob Python package. I scraped Yelp review data using the Yelp API.

This allows us to extract the key factors that matter the customer experience the most. Conducting sentiment analysis involves bag-of-word and bigram techniques, which collect all the unique words or bigram (a pair of consecutive words) and represent each text with a sequence of 0s and 1s where 1 represents the existence of the feature. Diving into the sentiments of the review texts allows us to see actual users’ thoughts. I wanted to see if the relationship between polarity and rating was consistent, and if not, what the trends actually were.

Polarity classifies whether the expressed opinion in a sentence is positive, negative, or neutral by assigning a number ranging from -1 to 1 taking strength of the opinion into account. Thus, I wanted to investigate more about the reviews and ratings each restaurant received and how they related to each other in three major areas: polarities, bag-of-words/bigram and length of the review texts. Yelp has not only changed how humans behave and interact with each other, but has also had a major impact on restaurants and their business as well. Studies have shown that an extra half-star on a review nets restaurants 19% more reservations (Tepper). It serves as a platform to view restaurant reviews, ratings from other users out of five stars, and photos. Yelp is an application people refer to when selecting businesses and restaurants to patronize. Nowadays, modern technology has affected people’s daily lives not only by providing information efficiently, but also influencing decision making.

Yelp api reviews