使用 TextBlob 进行情感分析中的极性计算

Question

使用Text Blob的PatternAnalyser如何计算句子中单词的极性？

Answer 1

TextBlob 内部使用 NaiveBayes 分类器进行情感分析，依次使用的naivebayes分类器是NLTK提供的。

查看 Textblob 情绪分析器代码 here。

@requires_nltk_corpus
    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

 #### THE CLASSIFIER USED IS NLTK's NAIVE BAYES #####

        self._classifier = nltk.classify.NaiveBayesClassifier.train(train_data)

    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)

        #### USE PROB_CLASSIFY method of NLTK classifer #####

        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")
        )

NLTK 的 NaiveBayes 分类器的来源是 here.。此 returns 概率分布用于 Textblobs 情感分析器返回的结果。

def prob_classify(self, featureset):

使用 TextBlob 进行情感分析中的极性计算

Polarity calculation in Sentiment Analysis using TextBlob

sentiment-analysis

textblob