with our first try of using naive bayes on vectorized tf-idf trigram featureswe get an accuracy of percent and / auc of percent looking at the / chart shown in the following screenshotit shows much more encouraging behavior than the plots we saw in the previous for the first timethe results are quite encouraging they get even more impressive when we realize that percent accuracy is probably never achievable in sentiment classification task for some tweetseven humans often do not really agree on the same classification label using all the classes but againwe simplified our task bitsince we used only positive or negative tweets that means we assumed perfect classifier that classified upfront whether the tweet contains sentiment and forwarded that to our naive bayes classifier sohow well do we perform if we also classify whether tweet contains any sentiment at allto find that outlet us first write convenience function that returns modified class array that provides list of sentiments that we would like to interpret as positive def tweak_labels(ypos_sent_list)pos ==pos_sent_list[ for sent_label in pos_sent_list[ :]pos | ==sent_label