pipeline pipeline([('all'all_features)('clf'clf)]if paramspipeline set_params(**paramsreturn pipeline training and testing on the combined featurizers gives another percent improvement on positive versus negative=pos vs neg = =pos/neg vs irrelevant/neutral = =pos vs rest = =neg vs rest = with these resultswe probably do not want to use the positive versus rest and negative versus rest classifiersbut instead use first the classifier determining whether the tweet contains sentiment at all ("pos/neg versus irrelevant/neutral"and thenwhen it doesuse the positive versus negative classifier to determine the actual sentiment summary congratulations for sticking with us until the endtogether we have learned how naive bayes work and why they are not that naive at all for training sets where we don' have enough data to learn all the niches in the class probability spacenaive bayes do great job of generalizing we learned how to apply them to tweets and that cleaning the rough tweetstext helps lot finallywe realized that bit of "cheating(only after we have done our fair share of workis okespeciallywhen it gives another improvement of the classifier' performanceas we have experienced with the use of sentiwordnet