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In the end, we discovered that there is a large amount of ambiguity amongst reviews and that there is no set standard for what reviews of a particular rating are comprised of. We conducted two experiments in our study: one to identify the correlation between the number of reviews passed to a model and the ability of the model to accurately predict ratings and the other to identify text feature extraction methods which produce the highest prediction accuracy of a model. The glee is associated with a successful search for information because the website provides complete overviews of the services of different companies.
This is a real flash of enlightenment, which screams: Eureka. Therefore, we sought out to determine if a machine learning algorithm could illuminate enough of a correlation, between a review and the rating of the review, in order to convert the reviews into a set of quantitative metrics based on the customer evaluations of the business. The Yelp logo is as simple and concise as the name. You can further benefit from Yelp’s prominence by adding one of their logo stickers to your brick and mortar. If you own a local business, having an active Yelp page with high ratings is a great way to increase traffic and encourage sales. We noticed that it is incredibly difficult to evaluate a business on Yelp given a long list of reviews. Yelp is one of the most popular review sites on the web with millions of unique users each month. As a part of this study, we examined the efficacy of machine learning to predict Yelp ratings of Yelp reviews.
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