| from nltk.probability import ELEProbDist, FreqDist | |
| from nltk import NaiveBayesClassifier | |
| from collections import defaultdict | |
| train_samples = { | |
| 'I hate you and you are a bad person': 'neg', | |
| 'I love you and you are a good person': 'pos', | |
| 'I fail at everything and I want to kill people' : 'neg', | |
| 'I win at everything and I want to love people' : 'pos', | |
| 'sad are things are heppening. fml' : 'neg', | |
| 'good are things are heppening. gbu' : 'pos', | |
| 'I am so poor' : 'neg', | |
| 'I am so rich' : 'pos', | |
| 'I hate you mommy ! You are my terrible person' : 'neg', | |
| 'I love you mommy ! You are my amazing person' : 'pos', | |
| 'I want to kill butterflies since they make me sad' : 'neg', | |
| 'I want to chase butterflies since they make me happy' : 'pos', | |
| 'I want to hurt bunnies' : 'neg', | |
| 'I want to hug bunnies' : 'pos', | |
| 'You make me frown' : 'neg', | |
| 'You make me smile' : 'pos', | |
| } | |
| test_samples = [ | |
| 'You are a terrible person and everything you do is bad', | |
| 'I love you all and you make me happy', | |
| 'I frown whenever I see you in a poor state of mind', | |
| 'Finally getting rich from my ideas. They make me smile.', | |
| 'My mommy is poor', | |
| 'I love butterflies. Yay for happy', | |
| 'Everything is fail today and I hate stuff', | |
| ] | |
| def gen_bow(text): | |
| words = text.split() | |
| bow = {} | |
| for word in words: | |
| bow[word.lower()] = True | |
| return bow | |
| def get_labeled_features(samples): | |
| word_freqs = {} | |
| for text, label in train_samples.items(): | |
| tokens = text.split() | |
| for token in tokens: | |
| if token not in word_freqs: | |
| word_freqs[token] = {'pos': 0, 'neg': 0} | |
| word_freqs[token][label] += 1 | |
| return word_freqs | |
| def get_label_probdist(labeled_features): | |
| label_fd = FreqDist() | |
| for item,counts in labeled_features.items(): | |
| for label in ['neg','pos']: | |
| if counts[label] > 0: | |
| label_fd.inc(label) | |
| label_probdist = ELEProbDist(label_fd) | |
| return label_probdist | |
| def get_feature_probdist(labeled_features): | |
| feature_freqdist = defaultdict(FreqDist) | |
| feature_values = defaultdict(set) | |
| num_samples = len(train_samples) / 2 | |
| for token, counts in labeled_features.items(): | |
| for label in ['neg','pos']: | |
| feature_freqdist[label, token].inc(True, count=counts[label]) | |
| feature_freqdist[label, token].inc(None, num_samples - counts[label]) | |
| feature_values[token].add(None) | |
| feature_values[token].add(True) | |
| for item in feature_freqdist.items(): | |
| print item[0],item[1] | |
| feature_probdist = {} | |
| for ((label, fname), freqdist) in feature_freqdist.items(): | |
| probdist = ELEProbDist(freqdist, bins=len(feature_values[fname])) | |
| feature_probdist[label,fname] = probdist | |
| return feature_probdist | |
| labeled_features = get_labeled_features(train_samples) | |
| label_probdist = get_label_probdist(labeled_features) | |
| feature_probdist = get_feature_probdist(labeled_features) | |
| classifier = NaiveBayesClassifier(label_probdist, feature_probdist) | |
| for sample in test_samples: | |
| print "%s | %s" % (sample, classifier.classify(gen_bow(sample))) | |
| classifier.show_most_informative_features() | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment