
Comparing AWS and GCP NLP API for Sentiment Analysis and a Case for Custom Model - prabhatjha
https://engineering.wootric.com/building-our-own-sentiment-analytics-model
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hachibu
What is transfer learning and why is better for customer feedback?

On a side note I'd love to see another head-to-head diagram of the transfer
learning against AWS and GCP.

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rsmith49
Transfer learning is the premise of learning from data in one domain, and then
being able to transfer that knowledge to another domain. In this case, we
leverage large existing text datasets to build an initial model, and then
tailor that model to customer feedback and apply it to our specific problem
(for this blog post, classifying feedback sentiment).

We chose to use this approach for customer feedback since it combines the
benefits of a general model (large amounts of data) with the benefits of a
domain-specific model (targeted to customer feedback). It looks like we were
validated, as it outperformed Google NLP API and AWS Comprehend - both general
models - on our set of customer feedback data.

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jumpingdeeps
I’m surprised AWS doesn’t expose NLP services tuned for customer feedback type
text data—-they surely must use/maintain models for this type of data
internally given the Amazon store is a trove of customer feedback training
data.

Separately, where does Azure fall into this space?

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rsmith49
I would imagine that AWS Comprehend utilizes a lot of customer feedback when
training its internal models. However, they do advertise Comprehend as a
general purpose NLP service, so they may have taken steps to make the models
generalize to the non-feedback domain. In any case, on our set of sentiment
data for customer feedback, Comprehend is outperformed by Google’s NLP API and
our own internal model.

As for Azure, we actually did not factor their service into our comparison.
This is definitely something to consider in future blog posts.

