This post is a great word2vec/keras intro, but it doesn't do one thing you should _always_ do before you break out the neural networks: try to solve the problem with traditional machine learning to establish a performance baseline and confirm the data is predictive.
78.9% accuracy on a sentiment classification of tweets with no neutral class is actually slightly _worse_ than you get if you do this in scikit-learn with plain old bag of words and Logistic Regression: https://github.com/williamsmj/sentiment/blob/master/sentimen....
78.9% accuracy on a sentiment classification of tweets with no neutral class is actually slightly _worse_ than you get if you do this in scikit-learn with plain old bag of words and Logistic Regression: https://github.com/williamsmj/sentiment/blob/master/sentimen....