How to Scrape Glassdoor with Selemium and BeautifulSoup

Glassdoor is a popular website that allows users to search for jobs, research companies, and read reviews from current and former employees. As a data scientist or analyst, you can scrape data from Glassdoor for various purposes, such as conducting sentiment analysis on employee reviews or collecting salary data for specific industries or companies. In this article, you will learn how to scrape Glassdoor website, what you can extract, and how to do it right.

   

What is Glassdoor?

Glassdoor is the ultimate insider’s guide to the world of work. It is a robust platform connecting job seekers with companies and employers, providing unparalleled transparency into what it’s like to work at a particular organization. Therefore, with its extensive database of company reviews, salary reports, and interview questions, Glassdoor provides job seekers with an inside look at the culture, values, and benefits of companies across various industries and sectors.

 

But Glassdoor is much more than just a job search engine. It’s a community of millions of professionals sharing their experiences and knowledge to help each other succeed in their careers. From expert career advice and tips to exclusive company reviews and ratings, Glassdoor is a one-stop shop for all your career needs.

 

Glassdoor is a valuable resource for businesses and researchers, providing access to employee reviews, salaries, job postings, and more. However, manually extracting this data can be a time-consuming and tedious process. You can automate data collection and analysis using web scraping techniques, saving time and improving accuracy.

Which Data Can Be Extracted By Scraping The Glassdoor?

Scraping any website is not the most trivial task due to a large amount of data and the site’s security measures, so you must be clear about precisely what data you need. Glassdoor website can provide access to a wide range of data, including:

 

What Do You Need To Scrape The Glassdoor?

To scrape the Glassdoor website, you will typically need a few essential tools and resources:

Web Scraping Software

A web scraping software program can help you automate collecting data from Glassdoor. There are many different web scraping tools available, both free and paid, that can help you scrape Glassdoor and other websites.

Proxy Server

A proxy server can help you avoid being blocked by Glassdoor or other websites during scraping. A proxy server allows you to route your web traffic through a different IP address, making detecting and blocking your scraping activities harder for websites.

Data Storage and Management

As you scrape Glassdoor, you will accumulate a large amount of data that needs to be stored and managed. You will need a database or other storage solution that can handle large amounts of data and allow you to quickly search, sort, and analyze the data you collect.

Programming Skills (optional)

While many web scraping tools allow you to scrape websites without writing any code, having some programming skills can help customize your tool and deal with any issues that arise during the scraping process. Next, we will look at several ways to scrape Glassdoor, with and without programming knowledge.

Knowledge of Web Scraping Laws and Ethics

It’s essential to be aware of the legal and ethical considerations surrounding web scraping, including data privacy laws and website terms of use. Violating these laws and terms can result in legal action and damage your reputation. You can also read the Terms of Use on the Glassdoor website.

How To Scrape A Glassdoor Without Coding?

Scraping a Glassdoor website without coding knowledge can be a challenging task. However, some online tools can help you scrape the data without any coding knowledge. Here are some steps to follow:

   

As you can see, many scraping services on the web can do all the hard work for you. You have to choose the right one.

How To Scrape A Glassdoor With Coding?

With programming knowledge, you will not have to deal with intermediaries (not considering the technical nuances like a proxy server) and pay for something you can create yourself. Scraping a Glassdoor website using various programming languages and libraries, such as Python, Beautiful Soup, and Selenium. Here are some steps to follow:

   

Next, you will see some code examples for BeautifulSoup and Selenium.

Scrape Glassdoor using BeautifulSoup

Here is a detailed guide on how to scrape Glassdoor using Beautiful Soup:

 

First, make sure you have the required libraries installed. You will need Beautiful Soup, requests, and pandas. You can install these using the following command:

   

pip install beautifulsoup4 requests pandas

 

Next, import the libraries into your Python script:

   

import requests

from bs4 import BeautifulSoup

import pandas as pd

 

Define the URL of the Glassdoor page you want to scrape. For example, if you want to scrape job listings in New York City for the keyword “data scientist,” you could use the following URL:

   

url = ‘https://www.glassdoor.com/Job/new-york-city-data-scientist-jobs-SRCH_IL.0,13_IC1132348_KO14,28.htm’

 

Use the requests library to retrieve the HTML content of the page:

   

response = requests.get(url)

html = response.content

 

Use Beautiful Soup to parse the HTML content:

   

soup = BeautifulSoup(html, ‘html.parser’)

 

Use the Chrome Developer Tools or a similar tool to inspect the page and identify the HTML tags and classes that contain the data you want to scrape. For example, if you want to scrape the job titles, you could use the following code:

   

job_titles = []

for div in soup.find_all(‘div’, {‘class’: ‘jobHeader’}):

for a in div.find_all(‘a’, {‘class’: ‘jobLink’}):

job_titles.append(a.text.strip())

 

This code finds all the <div> tags with the class ‘jobHeader’, then finds all the <a> tags with the class ‘jobLink’ within those <div> tags, and extracts the text of the <a> tags. It then adds the text to the job_titles list.

 

Repeat step 6 for any other data you want to scrape, such as company names, salaries, and job locations.

 

Once you have scraped all the data you want, you can store it in a pandas DataFrame:

   

df = pd.DataFrame({‘Job Title’: job_titles, ‘Company’: companies, ‘Salary’: salaries, ‘Location’: locations})

 

This code creates a DataFrame with the job titles, companies, salaries, and locations as columns.

 

You can then save the DataFrame to a CSV file:

   

df.to_csv(‘glassdoor_jobs.csv’, index=False)

 

This code saves the DataFrame to a file named ‘glassdoor_jobs.csv’.

Glassdoor Scraper on Selenium

This is a small guide on how to scrape glassdoor with Selenium.

Prerequisites

Before starting, make sure that you have the following:

 

Step 1: Import the Required Libraries

 

First, you need to import the necessary libraries in our Python script. In this case, you will need the Selenium and time libraries.

   

from selenium import webdriver

import time

Step 2: Set Up the Browser and Login

Next, set up the browser and log in to Glassdoor. First, instantiate a new instance of the browser using the webdriver module. Then navigate to the Glassdoor login page and enter credentials.

   

# Set up the browser

driver = webdriver.Chrome(‘/path/to/chromedriver’)

 

# Navigate to the Glassdoor login page

driver.get(‘https://www.glassdoor.com/profile/login_input.htm’)

 

# Enter login credentials

username = driver.find_element_by_name(‘username’)

password = driver.find_element_by_name(‘password’)

username.send_keys(‘your_email_address’)

password.send_keys(‘your_password’)

driver.find_element_by_class_name(‘gd-ui-button’).click()

 

# Wait for page to load

time.sleep(3)

 

Note that you will need to replace ‘/path/to/chromedriver’ with the path to your Chrome or Firefox webdriver.

Step 3: Search for Jobs

Once you have logged in, you can search for jobs on Glassdoor. To do this, navigate to the job search page and enter these search criteria.

   

# Navigate to the job search page

driver.get(‘https://www.glassdoor.com/Job/jobs.htm’)

 

# Enter job search criteria

search = driver.find_element_by_id(‘sc.keyword’)

search.send_keys(‘data analyst’)

location = driver.find_element_by_id(‘sc.location’)

location.clear()

location.send_keys(‘New York, NY’)

driver.find_element_by_id(‘HeroSearchButton’).click()

 

# Wait for page to load

time.sleep(3)

 

Step 4: Scrape Job Data

Now that you have performed a job search, you can scrape data on the job postings. You need to loop through each job posting on the page and extract the job title, company name, and salary information (if available).

   

# Loop through job postings and scrape data

jobs = driver.find_elements_by_css_selector(‘[data-test=”jobListing”]’)

for job in jobs:

# Get job title

title = job.find_element_by_css_selector(‘[data-test=”jobTitle”]’).text

 

# Get company name

company = job.find_element_by_css_selector(‘[data-test=”jobListingHeader”]’).text

 

# Get salary information (if available)

try:

salary = job.find_element_by_css_selector(‘[data-test=”salary”]’).text

except:

salary = ‘Not available’

 

# Print job data

print(‘Job title:’, title)

print(‘Company:’, company)

print(‘Salary:’, salary)

print(‘\n’)

 

Step 5: Close the Browser

Finally, close the browser to end the session.

   

# Close the browser

driver.quit()

 

That’s it! Note that this is just a basic example, and you can modify the script to scrape additional data or perform more complex searches.

How to Scrape Glassdoor – Conclusion

So, web scraping Glassdoor can be an efficient and effective way to collect valuable data for businesses and researchers. By following these steps and using the right web scraping tools, you can automate data collection and analysis, saving time and improving accuracy. 

 

Consequently, you have a choice: do the scraping with your own strength and skills, or use ready-made solutions from popular services. Whatever you choose, it is essential to consider that the extracted data’s speed, quality, and reliability depend on the experience and the necessary tools. Would you like to learn more about Glassdoor scraping and benefit from the expertise of our experts? Contact us for consultation.