In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python.
Coding Challenge for this video:
https://github.com/llSourcell/How_to_do_Sentiment_Analysis
Ludo's winning code:
https://github.com/ludobouan/pure-numpy-feedfowardNN
See Jie Xun's runner up code:
https://github.com/jiexunsee/Neural-Network-with-Python
Tutorial on setting up an AMI using AWS:
http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/
More learning resources:
http://deeplearning.net/tutorial/lstm.html
https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis
https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll
http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html
https://www.kaggle.com/c/word2vec-nlp-tutorial
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If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part.
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