np zeros( shape[ ] [pos astype(intreturn note that we are talking about two different positives now the sentiment of tweet can be positivewhich is to be distinguished from the class of the training data iffor examplewe want to find out how good we can separate the tweets having sentiment from neutral oneswe could do this as followsy tweak_labels( ["positive""negative"]in we now have (positive classfor all tweets that are either positive or negative and (negative classfor neutral and irrelevant ones train_model(create_ngram_modelxyplot=true as expectedthe / auc drops considerablybeing only percent now the accuracy is still highbut that is only due to the fact that we have highly imbalanced dataset out of , total tweetsonly , are either positive or negativewhich is about percent this means that if we created classifier that always classified tweet as not containing any sentimentswe would already have an accuracy of percent this is another example of why you should always look at precision and recall if the training and test data is unbalanced