NewsArticleClass.py 文件源码

def extractFeatures(self, article, n, customStopWords=None):
        # pass in article as a tuple ( text, title)
        text = article[0]
        # extract the text
        title = article[1]
        # extract the title
        sentences = sent_tokenize(text)
        # split text into sentences
        word_sent = [word_tokenize(sentences.lower()) for a in sentences]
        # split sentences into words
        self._freq = self._compute_frequencies(word_sent, customStopWords)
        # calculate word freq using member func created above
        if n < 0:
            # how many features (words) to return - a -ve number means
            # no feature ( word) selection, just return all features
            return nlargest(len(self._freq_keys()),
                            self._freq, key=self._freq.get)
        else:
            # here we say if calling e func has asked for a subset
            # then return only the 'n' largest features, i.e. the
            # most important words ( important == frequent, less stopwords)
            return nlargest(n, self._freq, key=self._freq.get)