Financial News Headline Sentiment Analysis (NLP) - Data Every Day

Hi guys, welcome back to Data Every Day!
On today's episode, we are looking at a dataset of financial news headlines and trying to predict the sentiment of a given headline. We will be using a TensorFlow RNN (GRU) to make our predictions.
Here is a link to the Kaggle dataset:
www.kaggle.com...
And here is a link to my notebook from the video:
www.kaggle.com...
Thanks so much for watching! If you enjoyed today's episode, be sure to subscribe and hit the bell for more content!
See you all tomorrow! :)
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[@sa12935]

I am getting : "NotImplementedError: Cannot convert a symbolic Tensor (gru_2/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported "
while running the traning code in jupyter notebook, please help

[@rhubarb2301]

great vid :)

[@ayencoscolfield3312]

Awesome goodness

[@neha-fp6kp]

is it supervised or unsupervised
?

[@gcdatkin]

Supervised. Since we are given the sentiment labels along with the headlines, we are able to learn from how our predictions differ from the labels.

[@neha-fp6kp]

@@gcdatkin thnks buddy