![]() If the function returns 200, that means that the request has executed successfully. You can check that your request was successful by checking the request status code. ![]() # If you’re using a different site just replace the url e.g. In this case, the url used points to Tesla dealerships on yelp. Then make a request to a the site that contains the reviews you want to extract. To do this, first start out by import the required modules. You can use the python Requests module to make a request to the website where the reviews are located and then use BeautifulSoup to traverse (read search through) the result to extract what you need. If not…and your business (or the business you want to analyse) has reviews on Yelp, Facebook Reviews or Google places you can build a quick scraper to get this data into a format that you can use. To load your data use the pandas from_csv method. ![]() Throw them all into a Excel Workbook or a CSV. If you’ve got them collated somewhere already that’s perfect. There’s a few different ways you can get access to your business reviews. Taking your raw reviews from a site like Yelp and leveraging modern Natural Language Processing tools to get clear and useful metrics around sentiment. This post goes through exactly how to do just that. Insights, that could help you drill into reviews that maybe weren’t so great. What if, you could throw your reviews into a black box and out popped out some intelligent insights. And when you’re running a new business, you just don’t have much of it lying around. Maybe, you’re spending too much time reading the good reviews and not surgically breaking down the negative ones. Maybe, there’s a pattern you’re not seeing. ![]() You’re still getting sucky reviews on Yelp. You shed blood, sweat and tears into build your team and delivering great service. You poured your heart and soul into producing a product ![]()
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