We look at how the location, keywords and attributes affect the success of the business. Here in the first phase, we focus on visualizations (graphs, plots and maps) to explore the data in a way that can be useful for further analysis. Some of the questions we will be interested in includes how well we can guess a review's rating from its text alone? What are the most common positive and negative words used in reviews? Can we extract tips from reviews? Is it possible to predict the closure of a business based on the reviews received? The idea is to find attributes that result in high ratings and thus suggest improvements in certain services in order to attract more customers.
Here, we plan to conduct a sentiment analysis of the text description of the reviews received for food businesses in Charlotte. The goal is to classify sentiments using the enormous review data text and predict the success or failure of a business. People can also vote on user reviews if they find it useful, funny or cool. The reviews consists of text description, star ratings, reviewer’s information, business descriptions for various categories (as defined by Yelp) etc. These reviews shared by service users are valuable for both business owners and prospective customers.
In today’s era of internet and information, it has become easier to find people with similar experiences you are looking for and website services like Yelp are playing an important role in making such information readily available. One’s ability of making decisions is largely dependent on others opinions with similar experiences.
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