Scrape Yelp listings without any code [Free]
You need a strategy for figuring out what your customers want, and you need to make sure you’re constantly adjusting and testing your approach. You can figure that out from price comparison, and competition marketing from the local listings like yelp. These business listings can also give you leads if you have B2B business model.
In order to get all the details from a website like a phone number, email, company name … is a tiring job. Most of the scrapers, usually use programing language to use these.
DataKund is a software, where you can automate scraping all these details without using any programing language with a single click. You can also personalize what you want to scrape.
Step 1: Install DataKund
- Click here “DataKund” to download.
- Install DataKund, After the installation, the automated browser will open with the DK extension in the taskbar.
Step 2: Create a new bot
- To create a new bot, click DK extension and type “yelp_listing” and then click “ + New API”.
Step 3: Train bot
- Now open yelp.com in the browser before training the bot, and click “Record” to train the bot.
Step 4: Reload the page to add the URL to the event.
Then add a static wait for 3 to 5 to allow page to load by right click on Datakund->Wait->Static->3.
Step 5: To train the bot to enter a keyword, press tab and then enter location and click on search to train the bot for inputs
Step 6: Then add a static wait for 3 to 5 to allow page to load by right click on Datakund->Wait->Static->3.
Step 7: Now right click on the website screen, then click Datakund->Start Repeat, to add loop for scraping similar items.
Step 8: Now click the keyboard icon to add the outputs such as listing
then click on save and back to recording
Step 9: Then open first listing
Step 10: Then right click on listing and then Datakund->Scrape->Text->listing, to scrape the listing name.
Similarly we can scrape service, description, serving_area.
The modules are well documented, it shows you how to collect the data, I want to improve it a little bit so that the data will be saved as CSV and you can use it for doing your research as a machine learning engineer. Here is the code that I created.