用Python爬蟲對豆瓣《敦刻爾克》影評進行詞雲展現

最近很想看的一個電影，去知乎上看一下評論，恰好在學Python爬蟲，就作個小實例。

代碼基於第三方修改 原文連接  http://python.jobbole.com/88325/#comment-94754

#coding:utf-8
from lib2to3.pgen2.grammar import line

__author__ = 'hang'

import warnings
warnings.filterwarnings("ignore")
import jieba    #分詞包
import numpy    #numpy計算包
import re
import pandas as pd
import matplotlib.pyplot as plt
import urllib2
from bs4 import BeautifulSoup as bs
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)
from wordcloud import WordCloud#詞雲包

#分析網頁函數
def getNowPlayingMovie_list():
    resp = urllib2.urlopen('https://movie.douban.com/nowplaying/hangzhou/')
    html_data = resp.read().decode('utf-8')
    soup = bs(html_data, 'html.parser')
    nowplaying_movie = soup.find_all('div', id='nowplaying')
    nowplaying_movie_list = nowplaying_movie[0].find_all('li', class_='list-item')
    nowplaying_list = []
    for item in nowplaying_movie_list:
        nowplaying_dict = {}
        nowplaying_dict['id'] = item['data-subject']
        for tag_img_item in item.find_all('img'):
            nowplaying_dict['name'] = tag_img_item['alt']
            nowplaying_list.append(nowplaying_dict)
    return nowplaying_list

#爬取評論函數
def getCommentsById(movieId, pageNum):
    eachCommentStr = ''
    if pageNum>0:
         start = (pageNum-1) * 20
    else:
        return False
    requrl = 'https://movie.douban.com/subject/' + movieId + '/comments' +'?' +'start=' + str(start) + '&limit=20'
    print(requrl)
    resp = urllib2.urlopen(requrl)
    html_data = resp.read()
    soup = bs(html_data, 'html.parser')
    comment_div_lits = soup.find_all('div', class_='comment')
    for item in comment_div_lits:
        if item.find_all('p')[0].string is not None:
            eachCommentStr+=item.find_all('p')[0].string
    return eachCommentStr.strip()

def main():
    #循環獲取第一個電影的前10頁評論
    commentStr = ''
    NowPlayingMovie_list = getNowPlayingMovie_list()
    for i in range(10):
        num = i + 1
        commentList_temp = getCommentsById(NowPlayingMovie_list[0]['id'], num)
        commentStr+=commentList_temp.strip()


    #print comments
    cleaned_comments = re.sub("[\s+\.\!\/_,$%^*(+\"\')]+|[+——()?【】《》<>,「」！，...。？、~@#￥%……&*（）]+", "",commentStr)
    print cleaned_comments
    #使用結巴分詞進行中文分詞

    segment = jieba.lcut(cleaned_comments)
    words_df=pd.DataFrame({'segment':segment})

    #去掉停用詞
    stopwords=pd.read_csv("D:\pycode\stopwords.txt",index_col=False,quoting=3,sep="\t",names=['stopword'], encoding='utf-8')#quoting=3全不引用
    words_df=words_df[~words_df.segment.isin(stopwords.stopword)]

    print words_df
    #統計詞頻
    words_stat=words_df.groupby(by=['segment'])['segment'].agg({"計數":numpy.size})
    words_stat=words_stat.reset_index().sort_values(by=["計數"],ascending=False)

    #用詞雲進行顯示
    wordcloud=WordCloud(font_path="D:\pycode\simhei.ttf",background_color="white",max_font_size=80)
    word_frequence = {x[0]:x[1] for x in words_stat.head(1000).values}

    word_frequence_list = []
    for key in word_frequence:
        temp = (key,word_frequence[key])
        word_frequence_list.append(temp)

    wordcloud = wordcloud.fit_words(dict(word_frequence_list))
    plt.imshow(wordcloud)
    plt.axis("off")
    plt.show()

#主函數
main()