Show HN: Summarizing product reviews into simple bullet-point lists with GPT-3

Hey HN!

Finding and researching good products can be very time-consuming and frustrating. Every time I want to buy a product, I waste hours reading reviews and researching the quality, durability and maintainability of it. Wouldn't it be great to have a service that does all this for me?

That's why I built the AI-Reviewer. The AI-Reviewer summarizes product reviews from all over the web into simple bullet-point lists.

#How does it work?

1. Scraping reviews from trusted sources on the web

2. Running it through a fake detection

3. Doing a sentiment analysis

4. The AI-Reviewer generates a brief and concise summary of all the reviews by using GPT-3

#What sources do I use?

I asked users where they look for product reviews and focused on the most trusted sources.

Besides the reviews of buyforlife, the sources are: Reddit, Wirecutter, Amazon, GearLab and some other.

#How do I prevent fake reviews?

This is a question that always comes up. There is no satisfactory solution to this problem, but I'm trying my best. A few things I'm doing:

- Running Amazon reviews through fakespot.com

- Diversifying sources and cross checking them

- Adding weight to reviews from trustworthy sources like Reddit and buyforlife.

#What's next?

I plan to continuously increase the number of products with an AI-Review. In addition, I can think of a few more use-cases:

- Summarizing warranty terms and conditions of brands into simple bullet-point lists

- Summarizing maintenance and care instructions

- Shopify App & Google Chrome Extension

- Ability to compare products

Read the full blog post here: https://www.buyforlife.com/blog/548RijnkRdPwn1cAI5RDjw/make-...

Looks awesome and good job on hands-on demos.

Is it possible to extend it to run statistical analyses to spot fake reviews without third party tools or at least discrepancies and contradictions among reviews?

Thanks for the feedback. Improving the fake review detection by using statistical analysis is definitely on my list once this gets past the MVP state. If anyone has inputs on how to implement this, you are welcome to contact me :)

Nice concept

It would be good to have links to the original reviews which were used for bullet points.