Amazon Comprehend

Discover insights and relationships in text

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#2 Product of the DayNovember 30, 2017

Amazon Comprehend is an NLP service that identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic.

  • John Gil
    John GilArchipella Studios (Owner)



    This is not blank...

    Allowing robots to do your creative thinking is an arena for the lazy and vulnerable mind. Of course this will not be understood now because all things new are exciting and the youth of today are unwittingly being drawn into a false collective. The day will come when a significant aspect of the individual identity, the very point of existence, is no more and the individual will lack the self cognizance to ask this question due to unawares. What remains at that point is obedience, a goal that the globalists of the war have been seeking for many centuries. Well done! Everyone has played their role to perfection. My friends remain the anomalies.

    John Gil has never used this product.
  • Pros: 

    Easy to use


    Consumption rate is not quite visible

    Lack of features

    I think this product lacks a dashboard showing how your consumption rate is. AWS Comprehend has 3 good features but it's more reasonable to use your own tool.

    Ricardo Prado has used this product for one month.
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Charlie Coppinger
Charlie Coppinger@thecoppinger · Shopify
I spent a few minutes trying to figure out how this might be useful from a marketing perspective, and here's what I came up with: I recently came across a copy-writing 'hack' of sorts, which involves collecting a large number of negative reviews from a similar product/service to which you are attempting to write sales copy for and using a simple word-density analyser to determine which are the main pain-points that the feedback revolves around. That way, in theory, you can ensure that you're speaking to your audiences actual pains, as opposed to your assumptions of what they are. This could be used for the same purpose, only on steroids 💪🏼
David J. Bradley, MBA
David J. Bradley, MBA@dbradleyri · Digital Marketing Strategy Expert
@thecoppinger Exactly the perspective I was looking at this with. Now, I'll wait for someone to list an iteration of this with their own tool that does that specifically...
Scott Bowler
Scott BowlerPro@scotty_bowler · Co-founder
Here's a screenshot of what Amazon Comprehend looks like/does by the way: Can't wait to experiment with this on - could provide enormous benefit and insights for our users
Samuel Roy
Samuel RoyPro@samuelroy_ · Co-founder & CTO
Other providers like and are great at this too. See how Recast automatically find entities like Amazon and even identify "north" and "south": This is what we use for, working well I can tell you!
Kristian Freeman
Kristian FreemanPro@imkmf · software developer, musician
If you'd like to play with this technology locally, Stanford CoreNLP is a great open-source framework (GPL license) for NLP processing:
Brennan Erbeznik
Brennan Erbeznik@brennanerbz · Co-Founder, Hashletes
@imkmf Let's see a performance comparison.
Martin H. Normark
Martin H. Normark@martinhn · Founder, @geteighty
Jacob Wallenberg
Jacob Wallenberg@jrwallenberg
@imkmf @brennanerbz @martinhn I've played around with a few different packages (CoreNLP + some derivatives, Indicoio, etc.) and was very pleased with the keyword and sentiment features of Comprehend. The sentiment feature especially seems far more accurate.
Brennan Erbeznik
Brennan Erbeznik@brennanerbz · Co-Founder, Hashletes
@imkmf @geteighty @jrwallenberg what’s remarkable is that not much has changed since the 1990s when the Stanford parser was born. We’re still only doing basic entity and sentiment analysis. Where’s the meaning?
Jacob Wallenberg
Jacob Wallenberg@jrwallenberg
@imkmf @geteighty @brennanerbz I can't speak for how sophisticated it was back then, but I find this stuff super helpful. I can barely write Python, but I'm able to automatically parse through thousands of customer reviews and extract what they're talking about and how they feel about it with a decent level of accuracy - using completely free resources!
Sebastian Dobrincu
Sebastian Dobrincu@sebyddd · Serial Entrepreneur. I turn💡 into 💰
This has the potential to make NLP somewhat more mainstream among tech companies. Curious to see what the adoption will look like.
Martin H. Normark
Martin H. Normark@martinhn · Founder, @geteighty
Isn’t there a way to help teach it to recognize new entities? Without that, moving into any sort of vertical, like legal, would be impossible. SpaCy for Python produces about the same entities as in the example, and has a simple training model.
Allen Bruns
Allen Bruns@allen_bruns
@martinhn I was successful in using Spacy with machine learning in the area of Legal Docs.