Spanish notes Analysis

Hello everyone

I don’t know how to do the following.

I’ve comments about a product and I want to analyze and grouping them by Price, product type and others.

The comments are in spanish. Is there any workflow that I can use to make the analysis.

Hi @Jalvear

I believe I have understood your question but nevertheless we would need to see a bit of your data to evaluate how complicated is to analyze and group it. Can you share your data here or is it confidential?

Thanks & regards,



I’m saharing you a sample of the data set.
Notes.xlsx (9.8 KB)


I think you are trying to do what this person is doing: Sentiment Analysis on Amazon Reviews - #3 by Andrea123

I’m attaching a modified workflow similar to what I did for Andrea123.
Topic_Modeling.knar.knwf (87.2 KB)

Topic Modeling (Topic Detection) is a fairly difficult task because it requires extensive knowledge of NLP (natural language processing). As well, the data should be related to the topic. From the data you provided, I don’t see price, product, etc often (less than 10 rows for price for example). These comments a bit off topic: “No conoce Bupa”, “Llamar el miércoles a las 5:30, envié correo”, “Cliente es fisioterapeuta de 41 años”. These comments are about products (people?) known, timing, and age, so that is what the model will attempt to extract.

I suggest first watching:

And then watching:

There might be other ways to extract that information (Regex for example).


Hi @Jalvear

Thanks for the data. Complementary to @victor_palacios, I’m posting here a different approach and possible solution based on the -Similarity Search- node:

20220318 Pikairos Spanish notes Analysis.knwf (392.3 KB)

This is a “recycled” solution which was already used in the thread below:

It may be interesting to have a look at the different posts of this thread too.

Hope it helps.




Thank you @aworker, @victor_palacios

This help is very useful for me, it is incredible that things like these can be achieved.
I appreciate your help in this solution.

I’ll put it into practice, thank you very much again


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