How To Web Scrape

  • 12/01/2023

How To Web Scrape

Yelp is one of the most popular review websites for businesses and services, making it a great source of data for web scraping. Businesses can use the data gathered from scraping Yelp to analyze customer sentiment, compare competitors and gain insights into customer preferences.

In this blog, we’ll look at the various aspects of scraping Yelp data.

Data To Scrape From Yelp

Being one of the largest directories of its kind, Yelp provides access to a wealth of information. Here are some of the important pieces of data you can scrape from Yelp:

  • Business ids
  • Business names
  • Reviews
  • Ratings
  • Photos
  • Contact information (phone, email)

These provide a great source of data for businesses looking to gain insights into their customers.

Access The Search Result Page On

We will begin with accessing the search result page of before moving forward to Yelp web scraping. Here is a specimen of what a search page on really looks like. Follow this link to navigate the same

Now, we move ahead. The next step is to make a new folder that goes with the name yelp_scraper, which shall contain a file named This new file will have the Yelp scraping Python code for this attempt. The code goes like this.

$ mkdir yelp_scraper

$ touch yelp_scraper/

In order to scrape Yelp, we must understand the requirements of three different Python libraries, namely Requests, BeautifulSoup, and regular expressions.

How would these extensions help us?

The BeautifulSoup extension will aid the process of extracting information from HTML amidst Yelp web scraping.

The Requests extension will help in downloading the webpage in Python.

The final Regular Expressions will be assistance in utilizing some operations that cater to the extraction process in BeautifulSoup.

Now, this is a default available by Python. Still, there will be a need to undertake an additional step of installing the remaining two, Requests and BeautifulSoup, which can be seamlessly executed by performing a PIP command in the input like.

$ pip install beautifulsoup4 requests

Traditionally, any normal web page is developed and comprises HTML (HyperText Markup Language), which interacts with the servers when any kind of URL is entered into a browser. Requests are the tool to download the HTML, which will be done by opening the file and running the code:

import requests

url = ""

html = requests.get(url)


So far, the process will successfully bring a heavy amount of textual data through the window in HTML format, which will be parsed.

Scraping Information For Restaurants From Result Page

Yelp has data of a diverse nature. So before proceeding further to understand Yelp scraper free, we break down the information that needs to be extracted. Here, after we scrape Yelp reviews, we go for the identity, number of reviews, vicinity, and the webpages.

Web scraping services USA are bound to have many parsers to access online information, and BeautifulSoup is one by HTML. BeautifulSoup is a library with many possibilities and a robust API making it a go-to choice for most of those who scrape Yelp.

Till this point, the installation of BeautifulSoup has been successfully completed, and the data through the HTML code is available in hand. This data needs to be analyzed to understand which of those pieces contain the tags that are necessary to extract the data you desire while Yelp scraping Python. However, BeautifulSoup will not be required to retrieve the search result information. The same will often be utilized with Yelp web scraping on predetermined restaurant web pages.

As you can see, each fresh search on Yelp just updates the results area. The remainder of the webpage remains intact and is not reloaded. This almost always signifies that the web page is requesting responses via an API and then modifying the site according to the results. Incidentally, the same is accurate in the attempts to scrape Yelp reviews.

The next step is to run the respective browser that is being used in this extraction process and visit the network option. The network requests made by the HTML and its inspection of the HTML are facilitated by most browsers' developer tools. Proceed with looking up a different location to scrape Yelp when the network tab is simultaneously open. At this point, a request to /search/snippet endpoint.

At this point, a JSON response should appear with all information for the search section. If this is achieved, Yelp web scraping will be much easier through JSON compared to HTML responses. An important observation if this URL shows a few dynamic elements.

Here, the location of any given locale can be looked up, but the URL should stay intact. This will be possible through a successful elimination of the parent_request_id with a decent URL. In this case, Firefox could be an ideal choice for JSON responses. The only deal is to put the restaurant names on the result page in the filter box that goes with the JSON key that has the required data.

All the search results are available in a listicle format at the searchPageProps -> mainContentComponentsListProps with elements that are not directly visible in the search results. These can be filtered with the items that have only the searchResultLayoutType key set to iaResult.

The files must have a code given ahead that will do a search for restaurants in Seattle, WA, with all the lists and names.

import requests

search_url = ""

search_response = requests.get(search_url)

search_results = search_response.json()['searchPageProps']['mainContentComponentsListProps']

for result in search_results:

    if result['searchResultLayoutType'] == "iaResult":


Here is the next use of Firefox in order to shuffle the JSON for identity, number of reviews, vicinity, and the web pages that decodes the keys with the data. Here is the structure of the keys.

Neighborhoods (list): result['searchResultBusiness']['neighborhoods']

Review Count: result['searchResultBusiness']['reviewCount']

Rating: result['searchResultBusiness']['rating']

Business URL: result['searchResultBusiness']['businessUrl']

This is how the final file must look like:

import requests

search_url = ""

search_response = requests.get(search_url)

search_results = search_response.json()['searchPageProps']['mainContentComponentsListProps']

for result in search_results:

    if result['searchResultLayoutType'] == "iaResult":





        print("" + result['searchResultBusiness']['businessUrl'])


This was about how to scrape Yelp reviews using the Yelp data scraper with the best web scraping services USA. Many other scraping elements, like Yelp email scraper and Yelp data scraper, can also be useful for immersive data acquisition.

If you need help scraping, feel free to contact BotScraper. We offer state-of-the-art web scraping services, including Yelp data extraction. Our experienced team of developers offers customizable web scraping solutions tailored to your specific needs. Contact us today and let our data extraction experts take care of the hard work for you.

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