We created a website that analyzes thousands of backpack reviews from Amazon, blogs, YouTube, Reddit, & more. All powered by the latest NLP.
baqpa.com displays the results of a custom pipeline we built that:
- Takes any backpack (i.e., Osprey Talon 33).
- Looks for reviews on marketplaces like Amazon, manufacturer sites, blogs, Reddit, and YouTube.
- Scores reviews based on helpfulness and sentiment.
- Breaks down reviews based on any given topic (i.e., comfort, zipper, etc.)
- Provides a concise summary using GPT-3.
This process is automated end-to-end and is being rolled out for any given product.
This is all powered by reviewr.ai (a company I'm helping build).
You can read more here: reviewr.ai/blog/using-ai-to-research-products-for-you
Next steps:
- Increase the number of backpacks to 1,000
- Include fake detection
As I was building something of a similar concept (without the review analysis part, more towards product search/search engine/discovery part), I find this really really cool!
I'm really curious about the tech stacks/technical difficulties that you guys face behind all these
Hey Jerry! We use Next.js/Algolia/MongoDB as the stack. Pros are that it's very customizable. Cons are that it's a lot of work to set up and manage.
I saw you found a good solution with Polymer Search (who I support as a consultant). Looks awesome!
Nice! I really like the UI and the simplicity of your site!
Yeah, I have recently migrated Burplist to Polymer Search. I am pretty happy with it so far! I've been looking for similar search solutions for months now and decided to settle with Polymer Search.
The problem I had with Algolia was the pricing. That aside, I was also considering using MeiliSearch, Typesense, and Relevance AI.