Analyzing Yelp can have massive advantages for businesses, but the process of extracting the data can be slow, and the volume of data overwhelming. Browsing through thousands of pages manually is time-consuming, and often can’t provide the complete and accurate data that businesses need.
Luckily, Yelp scraping offers a solution. In this guide we’ll look at how to automate the process to get the data you need quickly, legally, and effectively.
What is Yelp scraping?
Yelp is a high-demand online service that connects any users – mostly individuals – with businesses across many sectors, from restaurants and retail to hospitality, healthcare, and even professional services. Users can share their experiences with businesses through reviews and ratings, helping prospective customers make informed choices when trying new businesses.
Feedback shared on Yelp gives businesses the opportunity to reach new customers, and also make improvements to their operations based on real information. The actionable insights that can be extracted from data in this platform can be a very valuable resource for businesses.
Web scraping can automate the gathering of data from the public pages of Yelp. Specialist tools and scripts are used to fetch the required data, saving time that would otherwise be used in collecting the data manually. For more information, take a look at our article about the basics of web scraping.
Automating the web scraping process allows businesses to instead focus their energies on strategic planning and business development.
There are many data points that can be scraped from Yelp, some of which include:
- User profiles: Information about reviewers, including their activity, location, and preferences.
- Reviews and ratings: Insights into customer experiences, satisfaction levels, and sentiments.
- Business information: Details such as names, addresses, phone numbers, hours of operation, and services offered.
- Location data: Geographical details that allow for location-based analysis and mapping.
Similarly, scraping visual platforms like Pinterest can complement Yelp data by providing insights into customer preferences and trends through images, videos, and engagement metrics. Learn more about how to scrape Pinterest for additional consumer insights
Why scrape Yelp?
Scraping Yelp reviews and other data can provide valuable insights for many different purposes. Firstly, it can be used for competitor analysis, allowing businesses to monitor competitors’ ratings, reviews, and customer engagement to identify strengths and weaknesses.
Similarly, scraping reviews from other platforms like Facebook can offer additional insights into customer behavior and sentiment. Learn more about scraping customer reviews on platforms like Facebook in our guide.
Scraping Yelp can also be used for reputation and sentiment analysis. Businesses can use reviews to gauge public opinion about your business or competitors to improve services or address customer concerns.
It can help businesses to tailor marketing strategies based on customer preferences, trends, and geographic data to maximize impact, or for research purposes to understand industry trends and customer behavior patterns for strategic planning.
Finally, reviews and feedback can be used in product development, to identify customer pain points and preferences, guiding the creation of better products and services.
Web scraping from Yelp can provide detailed and dynamic insights on market trends that can enable businesses to refine strategies and address customer concerns for gaining a competitive advantage. It helps businesses to create new avenues of growth opportunities, for delivering better value to customers.
Interested in scraping more than just text and reviews? Our article on how to web scrape videos shows how to extract video content from different platforms.
Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.
Datamam’s CEO and Founder, Sandro Shubladze, says: “Yelp holds a great deal of structured data representing customer sentiment, preferences, and market dynamics. Competitor reviews provide insights into the demands of the market, while your customers’ feedback will guide improvements and innovation.”
“Systematic and responsible use of Yelp data empowers businesses to understand their audience better.”
How to scrape Yelp
Scraping Yelp involves gaining access to publicly available data from specific areas of the Yelp website. Automating the web scraping process can make the process much more efficient so that trends, sentiment, and competitive data can be reviewed in detail very quickly. There are a number of pages on Yelp that can be scraped and analyzed, including:
- Review pages: Customer opinions, ratings, and timestamps.
- Search results: Business listings, ratings, categories, and basic details.
- Profiles: Reviewer activity, locations, and preferences.
Understanding the data that can be extracted from the different sections of the website can help businesses pinpoint the data most relevant to your goals. Let’s take a look at how web scraping is done.
1. Set up and planning
The first step, as with any web scraping project, is to define your objectives and the type of data you need. Identify your target pages (e.g., restaurant reviews in a specific city), and review Yelp’s Terms of Service to ensure compliance. Consider using proxies to avoid detection.
2. Install tools
Next, you’ll need to set up your environment with the right tools for the job. Some of these include Python, a versatile programming language for scripting the scraping process, and Requests, a library to send HTTP requests and access web pages. Another is Beautiful Soup: A Python library for parsing and extracting data from HTML and XML files.
3. Extract data
Write scripts to fetch the targeted pages.
import pandas as pd
import requests
from bs4 import BeautifulSoup
# Define the URL
url = 'https://www.yelp.com/search?find_desc=&find_loc=New+York%2C+NY%2C+United+States'
# Fetch the webpage
response = requests.get(url)
# Check for a successful response
if response.status_code == 200:
print('Success')
# Parse Data
else:
print(f"Error: Received unexpected status code {response.status_code}.")
4. Parse data
Use Beautiful Soup to locate elements containing reviews, ratings, and other data.
soup = BeautifulSoup(response.text, 'html.parser')
items_list = []
items = soup.find_all('li', {'class': 'y-css-mhg9c5'})
for item in items:
items_dict = {
'Title': item.find('div', {'data-traffic-crawl-id': 'SearchResultBizName'}).find('a').text.strip(),
'Url': f"https://www.yelp.com{item.find('div', {'data-traffic-crawl-id': 'SearchResultBizName'}).find('a')['href']}",
'Reviews': item.find('div', {'data-traffic-crawl-id': 'SearchResultBizRating'}).find_all('span')[-1].text.strip(),
'Rating': item.find('div', {'data-traffic-crawl-id': 'SearchResultBizRating'}).find_all('span')[0].text.strip(),
'ReviewSnippet': item.find('div', {'data-traffic-crawl-id': 'SearchResultReviewSnippet'}).find('p').text.strip()
}
items_list.append(items_dict)
5. Store and use data
Save the extracted data in either CSV or JSON format, to allow easy and fast access and analysis when needed. Store it in a database for scalability.
df = pd.DataFrame(items_list)
df.to_csv('yelp_data.csv', index=False, encoding='utf-8')
Sandro says: “Scraping Yelp requires a structured approach to ensure efficiency and compliance. The key is in extracting data and preparing it for meaningful analysis.”
Is it legal to scrape Yelp?
When considering scraping Yelp, it’s very important to consider questions about the legality and ethics of the process. While scraping publicly available data is generally legal, this does depend on how the data is collected, the platform’s terms of service, and compliance with the relevant laws and regulations. Certain conditions must be met to ensure compliance when scraping public data from Yelp. Businesses must consider that the site explicitly prohibits unauthorized scraping in its Terms of Service. Violating these terms may result in account bans or legal action. Scraping that disrupts Yelp’s services, circumvents security measures, or violates data protection laws (e.g., GDPR or CCPA) can lead to legal consequences. To mitigate this risk, it’s crucial to adhere to Yelp’s policies and focus on publicly available information without bypassing restrictions. Yelp provides the Fusion API, an official and legal way to access data which allows developers to retrieve structured information such as business details, reviews, and ratings. The Fusion API can allow businesses access to Yelp data legally and ethically, in a structured manner, without risk of IP bans. However, it is important to remember that it does have some limitations, such as restricted access to historical or bulk data. To scrape Yelp responsibly, businesses should avoid looking to extract data behind paywalls or login barriers, respect rate limits, and avoid overloading Yelp’s servers. It is recommended to use proxies and user-agent rotation to adhere to the ToS.
Sandro says: “While scraping publicly available data is generally legal, businesses need to be very careful not to slide into practices that might be viewed as malicious or unethical, such as overloading servers.” “Collaboration with experienced specialists in data extraction guarantees compliance and efficiency, enabling businesses to gain valuable insights without crossing ethical or legal boundaries.”
To ensure compliance and efficiency, you can collaborate with trusted data extraction specialists like Datamam. With extensive experience in ethical web scraping, Datamam provides tailored solutions that meet legal standards while delivering actionable insights. For more information on how we can assist with your web scraping needs, contact us today!
