Source code for textblob.en.sentiments

# -*- coding: utf-8 -*-
"""Sentiment analysis implementations.

.. versionadded:: 0.5.0
"""
from __future__ import absolute_import
from collections import namedtuple

import nltk

from textblob.en import sentiment as pattern_sentiment
from textblob.tokenizers import word_tokenize
from textblob.decorators import requires_nltk_corpus
from textblob.base import BaseSentimentAnalyzer, DISCRETE, CONTINUOUS


[docs]class PatternAnalyzer(BaseSentimentAnalyzer): """Sentiment analyzer that uses the same implementation as the pattern library. Returns results as a named tuple of the form: ``Sentiment(polarity, subjectivity)`` """ kind = CONTINUOUS #: Return type declaration RETURN_TYPE = namedtuple('Sentiment', ['polarity', 'subjectivity'])
[docs] def analyze(self, text): """Return the sentiment as a named tuple of the form: ``Sentiment(polarity, subjectivity)``. """ return self.RETURN_TYPE(*pattern_sentiment(text))
def _default_feature_extractor(words): """Default feature extractor for the NaiveBayesAnalyzer.""" return dict(((word, True) for word in words))
[docs]class NaiveBayesAnalyzer(BaseSentimentAnalyzer): """Naive Bayes analyzer that is trained on a dataset of movie reviews. Returns results as a named tuple of the form: ``Sentiment(classification, p_pos, p_neg)`` :param callable feature_extractor: Function that returns a dictionary of features, given a list of words. """ kind = DISCRETE #: Return type declaration RETURN_TYPE = namedtuple('Sentiment', ['classification', 'p_pos', 'p_neg']) def __init__(self, feature_extractor=_default_feature_extractor): super(NaiveBayesAnalyzer, self).__init__() self._classifier = None self.feature_extractor = feature_extractor @requires_nltk_corpus
[docs] def train(self): """Train the Naive Bayes classifier on the movie review corpus.""" super(NaiveBayesAnalyzer, self).train() neg_ids = nltk.corpus.movie_reviews.fileids('neg') pos_ids = nltk.corpus.movie_reviews.fileids('pos') neg_feats = [(self.feature_extractor( nltk.corpus.movie_reviews.words(fileids=[f])), 'neg') for f in neg_ids] pos_feats = [(self.feature_extractor( nltk.corpus.movie_reviews.words(fileids=[f])), 'pos') for f in pos_ids] train_data = neg_feats + pos_feats self._classifier = nltk.classify.NaiveBayesClassifier.train(train_data)
[docs] def analyze(self, text): """Return the sentiment as a named tuple of the form: ``Sentiment(classification, p_pos, p_neg)`` """ # Lazily train the classifier super(NaiveBayesAnalyzer, self).analyze(text) tokens = word_tokenize(text, include_punc=False) filtered = (t.lower() for t in tokens if len(t) >= 3) feats = self.feature_extractor(filtered) prob_dist = self._classifier.prob_classify(feats) return self.RETURN_TYPE( classification=prob_dist.max(), p_pos=prob_dist.prob('pos'), p_neg=prob_dist.prob("neg") )