def classifier_for_training_set(positive, negative, blacklist=[]):
"""
Returns a Bayesian classifier for the given positive and negative sentences.
"""
positive_feedback \
= map(lambda s: (FreqDist(tokenize(s, blacklist)).keys(), 'positive'),
positive)
negative_feedback \
= map(lambda s: (FreqDist(tokenize(s, blacklist)).keys(), 'negative'),
negative)
training_set = apply_features(_classifier_features_for_document,
positive_feedback + negative_feedback)
return nltk.classify.NaiveBayesClassifier.train(training_set)
def positive_sentiment_for_sentences(classifier, sentences, blacklist=[]):
"""
Returns the ratio of positive sentiment for the given list of sentences
between 0 and 1 inclusive.
"""
statistics = defaultdict(int)
for sentence in sentences:
tokens = tokenize(sentence, blacklist)
freq_dist = nltk.FreqDist(tokens)
classification = classifier.classify(_classifier_features_for_document(freq_dist))
statistics[classification] += 1
if len(statistics) == 0:
return 0
positive = statistics['positive']
negative = statistics['negative']
return positive / float(positive + negative)
mkdir(data_dir)
mailbox = mbox(argv[1])
for index, message in enumerate(mailbox):
payload = message.get_payload(decode=True)
if not payload:
continue
try:
body = unicode(payload, get_charset(message), 'replace')
except UnicodeEncodeError:
continue
if not body:
continue
clean_msg = preprocess(body)
sentences = split_sentences(clean_msg)
tokens = tokenize(clean_msg, blacklist)
bigrams = bigrams_for_message(sentences)
with open(path.join(data_dir, "preprocess-%0.4d.json" % (index + 1)),
'w') as f:
f.write(
json.dumps({
'sentences': sentences,
'tokens': tokens,
'bigrams': bigrams
}).encode('UTF-8'))
data_dir = path.abspath('./data')
if not path.isdir(data_dir):
mkdir(data_dir)
mailbox = mbox(argv[1])
for index, message in enumerate(mailbox):
payload = message.get_payload(decode=True)
if not payload:
continue;
try:
body = unicode(payload, get_charset(message), 'replace')
except UnicodeEncodeError:
continue
if not body:
continue
clean_msg = preprocess(body)
sentences = split_sentences(clean_msg)
tokens = tokenize(clean_msg, blacklist)
bigrams = bigrams_for_message(sentences)
with open(path.join(data_dir, "preprocess-%0.4d.json" % (index + 1)), 'w') as f:
f.write(json.dumps({
'sentences': sentences,
'tokens': tokens,
'bigrams': bigrams
}).encode('UTF-8'))
