How to extract product reviews automatically with AI to analyze pros and cons

Table of Contents

Extracting product reviews from online shops has become one of the vital activities of e-commerce SEO and an essential source of competitive intelligence for any product marketer.

You will no doubt know that they are an essential part of the shopping experience for users.

But they also play an important role in Product Page Positioning (PDP) by adding genuine, free content about the product.

However, manually analyzing these reviews is a nightmare for anyone. Users, SEOs, e-commerce managers etc. can spend hours and days analyzing the content of these reviews. 

Well, this is where our script comes in:  

This Colab works as a product review extractor that, supported by AI, uses natural language processing (NLP) to extract valuable information from customer reviews and return them in a summary list of pros and cons.

What is a Product Review Extractor?

A product review extractor is a tool that uses Natural Language Processing (NLP) and Machine Learning (ML) algorithms to automatically extract product reviews from customer feedback on e-commerce websites. 

These programs can analyze and classify reviews based on sentiment, keywords and topics, among other things.

As programmed.

In our case, the script we have prepared allows us to automatically generate a list of pros and cons, which users have reviewed in order to use it as a table in our product descriptions.

Or also to identify problems in the offer or service to improve products and services.

In fact, this tool can be customized to meet the specific needs of each professional profile, company or user interest.

The importance of evaluating product reviews in SEO

Analyzing product reviews is essential from an SEO point of view. There are several reasons for this:

1. Improve search engine positioning:

Product reviews can play an important role in improving your search engine rankings. 

This is because search engines like Google use customer reviews as a signal of the quality and relevance of your product pages. 

By analyzing these reviews and incorporating relevant keywords and phrases into your product descriptions, you can optimize your content for search engines and improve your ranking.

2. Provides valuable user-generated content:

User-generated content, such as product reviews, is invaluable because it adds credibility to product listings, helping to establish trust with potential customers.

From an SEO point of view, this is invaluable.

By analyzing these reviews and incorporating customer-rated attributes into product descriptions, it helps to highlight and improve conversions. 

Improving the user experience also has an impact on search engine rankings and driving traffic to your website.

3. Helps to identify and address customer concerns:

Finally, analyzing product reviews also helps to identify and address any customer issues with products. 

By understanding what they like and dislike about them, we can make improvements and updates to them. 

Or detect problems related to delivery service, or product quality. 

Overall, analyzing product reviews is essential not only for SEO, but for any online shop manager. 

By understanding what customers are saying, you can optimize your content and provide the most valuable information, improving your response to users’ search intentions and clearing objections to improve search engine rankings and conversions.

What you need to extract product reviews from a URL

Actually, all you will need are 3 things:


  1. Your OpenAI API Key
  2. The product URL to extract the reviews
  3. And install the Google Colab dependencies.


At this point, we have prepared an explanatory video created by our colleague Luis, who has also prepared the script to make everything much easier for you.

If you have never used a Colab before, we recommend you to watch it.

You can watch it right here:

As you can see, it’s all very simple.

However, we recommend you not to use it in an online shop like Amazon, eBay, or harpersbazaar because they usually have anti-scraping systems and probably the libraries we have used in this Colab are not enough to avoid such systems.

For these cases we recommend you to use Selenium and a couple of Proxies, you can scrape as much as you want without being blocked.

In any case, use it wisely and without abusing it to avoid crashes.

How our automatic review extractor works

Our e-commerce product review extractor is based on natural language processing with OpenAI’s GPT.

We have included the tiktoken libraries to count tokens and avoid exceeding the maximum limit of 4048 tokens in GPT3.5. 

Remember that you need to leave room for the response, so don’t reach the limit and don’t forget to insert your API KEY and the URL to parse.

It is also possible to change the model and the number of reviews to analyze. 

In the video example, we have used 4, but more can be analyzed. 

Here you can see an image of the generated prompt:

And here is a screenshot of the result:

As you can see, at the end, you will get a list of pros and cons for the page provided in Spanish.

Although you can configure the Colab or use a text translator into English.

The truth is that with our script it is very practical and simple.

You only need to select the URL from which you want to extract the data, enter it in the Google Colab together with your OpenAI API Key and press the button to process the information.

This way, you can quickly analyze any URL and obtain a summary with all the strengths and weaknesses of the product to:

  • Draw conclusions.
  • Understand what users value
  • Identify potential improvements of the product/service
  • Create a pros and cons table for your product description.
  • Incorporate valuable attributes that you have not considered in the content.

Benefits of using a product review extractor

Increased accuracy and efficiency in extracting product reviews

AI product review extractors can extract product reviews from large amounts of customer feedback accurately and efficiently, saving companies time and money. 

They are also able to recognize patterns and trends in customer reviews, allowing companies to identify common problems and themes.

Ability to analyze large amounts of data quickly

Analyzing customer feedback, providing companies with up-to-date information on product performance, is another capability of these applications. 

The information can be used to improve product development and customer service.

Cost-effective compared to manual extraction methods

AI product review extractors are significantly more cost-effective than manual extraction methods, reducing the need for human resources and increasing productivity.

Improving product page content and customer experience on e-commerce websites

An extractor can provide companies with information about the likes and dislikes of their customers, allowing them to make informed decisions about product development and marketing. 

This information can also be used to optimize product pages and provide customers with a better shopping experience.

Better understanding of the likes and dislikes of your supply side

They help organizations identify areas for improvement in their products and services, enabling them to make changes that improve customer satisfaction and increase sales.

Other use cases and applications

There are several use cases for an e-commerce product review extractor with AI apart from the ones mentioned above. 

Some of them have a lot to do with:

  • Market research: analyze customer opinions and feedback and gain valuable information on market trends and customer preferences.
  • Product improvement: Use the information generated by the extractor to identify product problems and improve product quality, durability, performance and other aspects of the product.
  • Competitor assessment: Companies can use an AI-enabled e-commerce product review extractor to analyze customer reviews of competitors’ products and compare them to their own products.
  • Business decision-making: use the information generated to make informed decisions about product improvement, customer satisfaction and marketing strategy.

Potential challenges and limitations of AI product review extractors

While AI product review extractors offer significant advantages, there are also potential challenges and limitations to consider, including the following:

1.Ethical concerns regarding the use of AI technology

There are ethical concerns regarding the use of AI technology, especially with regard to privacy and bias. It is essential to ensure that the tool is used in an ethical and transparent manner.

2.Limitations in accuracy and efficiency

AI product review extractors may not be entirely accurate or effective, especially when analyzing complex customer reviews. It is essential to ensure that the tool is continually updated and maintained to ensure its accuracy and effectiveness.

3.Need for continuous updates and maintenance

The Extractor requires continuous upgrades and maintenance to ensure that it remains effective and up to date with the latest technologies and customer needs.

Bottom line

In general, the use of a product review extractor provides valuable product information in a short time.

They make possible to perform tasks that were previously only available to a few companies and organizations with large marketing budgets.

Now, however, the playing field is more level.

Think about it for a moment:

Before, to get a list like the one provided by our extractor, you would have to:

  1. Search for review websites or e-commerce websites with user reviews
  2. Read the reviews available on e-commerce websites. You could use filters or sorting options to display only the most relevant reviews based on criteria such as date, rating or relevance.
  3. Identify common topics or issues using tools such as word clouds, text analysis software or manual tagging to categorize reviews.
  4. Extract pros and cons mentioned in the reviews based on the identified topics by deploying a spreadsheet or similar tool to group the pros and cons for future analysis.
  5. Analyze the data looking for patterns or trends using statistical analysis software or visualization tools to create charts and graphs.
  6. Draw conclusions based on the data analyzed.

As you can understand, this process is a drain on time and resources that you can invest on other tasks.

Thus, we hope you see how useful our extractor can be.

 Now, we are sure you do!

Would you like us to help you optimize your product descriptions with AI?

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Alvaro Pena
Head of SEO and co-CEO en iSocialWeb

Co-CEO and Head of SEO at iSocialWeb, an agency specialized in SEO, SEM, and CRO that manages over +350M organic visits per year with a 100% decentralized infrastructure. Also, the founder of Virality Media, a company with over 150 million monthly active visits across various sectors and industries. A Systems Engineer by training and an SEO by vocation. An avid learner, AI enthusiast, and dreamer of prompts.

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