How to Scrape Reviews: A Comprehensive Guide for Beginners

Many professions in IT and outside are relying more and more on software solutions. Financial analysts use statistical software like SPSS, and writers are sometimes expected to know some kind of content management system, like WordPress. Web scraping is one of those tech tools that is often used in non-tech positions, like scraping review data.

The Harvard Business Review outlines that 98% of shoppers read product reviews before making a purchase. If you notice your potential customers going all the way up the sale pipeline but stopping before making a purchase, it might be due to negative reviews or simply the lack of reviews at all.

In that case, you may want to look at competitors’ reviews for success stories, but once you go to Amazon, you will be overwhelmed by the abundance. Instead of wasting time manually, you can scrape product reviews with an automated web scraping tool. Whether you’re targeting Amazon, Best Buy, or social networks, you can get review data much faster. This article shows you where to begin.

Tools and Technologies for Scraping Reviews

Web scraping is an automated process of extracting online information using dedicated web scraping tools. There are a variety of them, like scraping bots, Python libraries, no-code scrapers, and API scrapers. You may also often encounter residential proxies , but that’s a different topic. You can learn more about it in our article about IP rotation for web scraping .

The short lesson here is that automated review data extraction is a process that requires specialized software and some know-how. Your chosen tools depend on scraping knowledge, like using Python coding language to set your unique web scraping rules (for advanced users) or using a no-code browser extension with limited scraping customization (for newcomers). Here are some of the most popular web scraping tools to give you a better idea.

Popular Web Scraping Tools

Web scraping can be challenging, especially for businesses that require vast amounts of structured data. Luckily, web scraping is more accessible than ever, largely thanks to many beginner-friendly scrapers. Here are three popular scraping tools to extract reviews.

Beautiful Soup

Beautiful Soup is Python’s parsing library. Parsing means converting one data format into another, usually unstructured web data, into a structured format. Beautiful Soup is designed to extract unstructured HTML and XML data (which is the core of a website) into a format that is more visually representative and searchable.

Although it is not a no-code solution, Beautiful Soup is easy to learn. Experienced scraping specialists will have to learn some Python one way or another, and Beautiful Soups is a great tool to start if you’re heading this path. However, it works best for small-scale data gathering, unlike Scrapy.

Scrapy

Scrapy is a fully developed Python scraping framework, so it can achieve more than Beautiful Soup but requires expertise. It can handle large-scale projects and is highly customizable, with features like error handling. Similarly, it is also developed for HTML and XML data extraction.

This tool has a fierce learning curve and requires a somewhat lengthy setup. Tech-savvy users use Scrapy to build proprietary web crawlers with unique rules and goals that better reflect project needs. As you can see, it requires active user participation, so let’s take a look at a more automation-oriented tool, Selenium.

Selenium

Most websites have anti-scraping protection to limit information sharing with competitors. Selenium is a browser automation tool that lets users simulate user interaction. Websites use behavioral analysis to identify and block scraping bots, which Selenium helps avoid.

With it, you can mimic form filling, button clicking, and other human-like actions. Compared to Scrapy, it can target more dynamic websites, going beyond Scrapy’s static HTML and XML data grabbing.

To summarize, all three tools have pros and cons, which are beneficial to customers. Additionally, you can visit our no-code web scrapers guide if you want to avoid dealing with programming languages. Now, let’s take a look at another online data exchange tool, APIs.

APIs for Review Scraping

API stands for Application Programming Interface. Imagine APIs as data pipelines between two consenting parties. For example, Amazon provides a public API called Product Advertising API (PA API). You agree with Amazon’s API rules, and it lets you collect some information from its massive platform. An API controls this data exchange.

But you will quickly run into trouble using it to gather Amazon product reviews. You may get limited data, like the number of reviews or star ratings, but it will not provide thousands of reviews for consumer sentiment analysis. Although API is a more transparent and efficient data-gathering tool, it is also limited and cannot scrape Amazon product reviews.

To give you a better idea, here’s a brief comparison of two popular APIs.

Yelp API and Google Places API

As the name suggests, the Yelp Fusion API gives access to Yelp’s database and Google Places API to Google’s vast repository. Although they are similar, there are crucial differences at a closer look.

Yelp provides more detailed information, mainly focused on local restaurants and other local businesses. It also lets you use vast user-generated content, which forms the core of its service. However, its worldwide information reach leaves some room for improvement.

On the other hand, Google is everywhere. It is much more suited for global information gathering, like overall restaurant spread in selected countries. Google Places API is also a priority tool if you want to use Google Maps data in your business. On the downside, the information is significantly more limited and does not include first-hand opinions found on Yelp.

How to Scrape Reviews?

Following are the general guidelines for scraping customer reviews for newcomers. Keep in mind that the exact workflow heavily depends on the chosen tools, but these steps will get you on your way.

Preparing for Scraping

Firstly, identify your target websites because the tools will depend on them. For example, social networks like X or LinkedIn display a lot of dynamic content, which can also be accessible only after a brief interaction. In this case, Selenium is a good choice.

Meanwhile, Amazon is much more static, so you can use Beautiful Soup to scrape Amazon reviews. At this stage, ensure that the chosen website hosts high-quality content because what good is a dataset if it is full of wrong or outdated information?

Also, narrow down data only to the required elements (like review text instead of the entire HTML document) to reduce bandwidth consumption, speed up the project, and adhere to lawful regulations like the California Consumer Privacy Act .

You can now set up a scraping environment. Choose a programming language (we focused on Python, but you can use JavaScript, Ruby, etc.). Download and install frameworks like Scrapy, and prepare all other tools, like residential proxies, anti-detect browsers, and no-code browser extensions.

Writing the Scraper

You may want to skip this step if you only need a scraping tool to occasionally grab customer reviews from accessible sources. However, if you’re going for in-depth customer feedback, like tens of thousands of Amazon reviews, you will need some customization.

If you decide to go this way, Beautiful Soup is one of the best choices for starters. Familiarize with requests.get(url) function that issues an HTTP GET request for a specific website. Then, proceed to learn data parsing to extract the required information from the HTML file.

While learning the basics, take a look at how to handle pagination. Because many websites display information on multiple pages, you must instruct your scraper how to go from one to the other. You can also take a look at dynamic content scraping using Selenium; it is very useful if you’re not afraid of tough challenges early on.

Storing the Data

Lastly, you must decide which format you will store the data in. THE CSV (comma-separated values) is one of the most popular formats that is easily readable and well-suited for data analysis. Meanwhile, the JSON and XML formats are great for working with web applications. Once again, the storage format depends on your project goals.

Overcoming Common Challenges in Review Scraping

Businesses want to collect user data, but they also want to keep it hidden from the competition. That’s why you can run into numerous challenges when trying to get customer reviews. Here are the most common scenarios you should be aware of.

Handling Anti-Scraping Measures

In most cases, you will have to remain undetectable when scraping a website. Firstly, use proxy servers to obfuscate your original IP address, as data-gathering requests from the same IP will quickly get you banned. Simultaneously, make sure you use a user-agent spoofer that will imitate genuine browsers so it looks like a person is browsing the website, and nothing more.

Set an ethical scraping request limit. For example, if you send too many concurrent requests in short intervals, they can overload the website, making it slower. Instead, respect the website owners and put some time between requests according to the magnitude of your project. If you notice you are still bombarded with CAPTCHAs, consider Selenium to implement an automated CAPTCHA solver.

Dealing with Dynamic Content

Dynamic websites are much more challenging because they display part of the content after the website has loaded. This renders tools like Beautiful Soup useless and requires additional effort. Be prepared to deal with JavaScript coding language, which is used by a majority of dynamic websites. The chances are good that you will also be required to mimic human behavior, as explained in the previous chapter.

Ensuring Data Quality

Once you have finished scraping customer reviews, it’s time to ensure the dataset is ready for further use. Firstly, clean the data by removing whitespaces, tabs, tags, and other unnecessary characters. Remove duplicate values, set missing value handling rules, and decide on the data storage format. Remember, you can automate the task with tools like OpenRefine, Pandas, etc.

The data validation is one of the last steps, but in ideal circumstances, a continuous one. Ensure all data is converted to an agreed format and uses a standardized naming convention. If you continuously cross-reference it with other datasets for inaccuracies, you can be sure your data is always up to date.

Review Scraping Use Cases

There’s no denying that the contemporary market is fiercely competitive. Dozens and hundreds of companies provide similar services, trying to stand out from the others. Gathering customer reviews is one of the best ways of knowing what consumers like.

Brand monitoring is one of the most popular review scraping use cases. It lets businesses see what users are saying about their products or take a look at competitor reviews. Automated review gathering enables you to compare thousands of reviews within minutes to adjust development or marketing strategies.

No less important is market research and product comparison. You can get pricing information to set yours accordingly, identify marketing trends to keep up, and monitor the competition whenever you see their sales increasing.

Conclusion: Legal and Ethical Review Scraping

Web scraping is a legal and powerful tool, but it must be used according to the law. To be on the safe side, avoid scraping personally identifiable data whenever possible. The European General Data Protection Regulation (GDPR) has strict rules on gathering and storing personal data, and you risk legal issues if you don’t adhere to these principles. For example, when scraping Amazon reviews, do not gather any personal information, like user names, profile details, etc.

You must also respect the website’s Terms of Service —the robot.txt file instructs which parts can be scraped. Even if you decide to go outside the set boundaries, do not overload the website or download copyrighted material or customer reviews that are not public information.