the devastating results for positive tweets against the rest and negative tweets against the rest will improve if we configure the vectorizer and classifier with those parameters that we have just found out=pos vs rest = =neg vs rest = indeedthe / curves look much better (note that the graphs are from the medium of the fold classifiersthus have slightly diverging auc values)neverthelesswe probably still wouldn' use those classifiers time for something completely differentcleaning tweets new constraints lead to new forms twitter is no exception in this regard because text has to fit into characterspeople naturally develop new language shortcuts to say the same in less characters so farwe have ignored all the diverse emoticons and abbreviations let' see how much we can improve by taking that into account for this endeavorwe will have to provide our own preprocessor(to tfidfvectorizer