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Thanks! The basic (default) version for the sentiment analysis is based on TextBlob library, but you can choose to activate deep learning to analyze sentiment with Google AI's BERT (trained on Twitter messages), though it is quite slow at the moment because inferences are made on a CPU and not a GPU.

The back-end is just Python/Flask and I use the free Algolia and Pushshift.io APIs to source the messages from HN and Reddit (big thanks to them!)

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This looks really great with such a simple UI.last year I tried to do a realtime sentiment analysis on twitter messages using TextBlob.It was fast but not very accurate. Can you suggest any other library which might works fast enough on realtime messages.

For inference speed I recommend a Naive Bayes model. I've tried this on Twitter messages and got near ~90% accuracy with 3-class (positive, negative, neutral).

The easiest library to do that would probably be scikit-learn with their ComplementNB class: https://scikit-learn.org/stable/modules/generated/sklearn.na...

For the data you can use the SemEval 2017 Task4-A dataset (around ~10K labeled tweets): https://github.com/cbaziotis/datastories-semeval2017-task4/t...

Thanks for the answer!

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