def clean_text(text, remove_stopwords=False):

    """

    数据清洗

    """

    text = BeautifulSoup(text, 'html.parser').get_text()

    text = re.sub(r'[^a-zA-Z]', ' ', text)

    words = text.lower().split()

    if remove_stopwords:

        words = [w for w in words if w not in eng_stopwords]

    return words

def to_review_vector(review):

    """

    获取词向量

    """

    global word_vec

    review = clean_text(review, remove_stopwords=True)

    #print (review)

    #words = nltk.word_tokenize(review)

    word_vec = np.zeros((1,300))

    for word in review:

        #word_vec = np.zeros((1,300))

        if word in model:

            word_vec += np.array([model[word]])

    #print (word_vec.mean(axis = 0))

    return pd.Series(word_vec.mean(axis = 0))