豆瓣电影《杀破狼》影评制作词云 -《狗嗨默示录》-

shapolang.py

# !/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'LiGoHi'
 
import warnings
warnings.filterwarnings("ignore")
import jieba    #分词包
import numpy    #numpy计算包
import codecs   #codecs提供的open方法来指定打开的文件的语言编码，它会在读取的时候自动转换为内部unicode
import re
import pandas as pd  
import matplotlib.pyplot as plt
from urllib import request
from bs4 import BeautifulSoup as bs
# %matplotlib inline
# from scipy.misc import imread
 
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)
from wordcloud import WordCloud#词云包
 
#分析网页函数
def getNowPlayingMovie_list():  
    resp = request.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
 
#爬取评论函数
def getCommentsById(movieId, pageNum):
    eachCommentList = [];
    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 = request.urlopen(requrl)
    html_data = resp.read().decode('utf-8')
    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:    
            eachCommentList.append(item.find_all('p')[0].string)
    return eachCommentList
 
def main():
    #循环获取第一个电影的前10页评论
    commentList = []
    NowPlayingMovie_list = getNowPlayingMovie_list()
    for i in range(10):    
        num = i + 1
        commentList_temp = getCommentsById(NowPlayingMovie_list[0]['id'], num)
        commentList.append(commentList_temp)
 
    #将列表中的数据转换为字符串
    comments = ''
    for k in range(len(commentList)):
        comments = comments + (str(commentList[k])).strip()
 
    #使用正则表达式去除标点符号
    pattern = re.compile(r'[\u4e00-\u9fa5]+')
    filterdata = re.findall(pattern, comments)
    cleaned_comments = ''.join(filterdata)
 
    #使用结巴分词进行中文分词
    segment = jieba.lcut(cleaned_comments)
    words_df=pd.DataFrame({'segment':segment})
 
    #去掉停用词
    stopwords=pd.read_csv("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)]
 
    #统计词频
    words_stat=words_df.groupby(by=['segment'])['segment'].agg({"计数":numpy.size})
    words_stat=words_stat.reset_index().sort_values(by=["计数"],ascending=False)

    # 设置背景图片
    # alice_coloring = imread("相片.png")
    #用词云进行显示
    wordcloud=WordCloud(font_path="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)
    #     word_frequence_list = word_frequence_list
    # print(word_frequence_list)

    wordcloud = wordcloud.fit_words(word_frequence)
    plt.imshow(wordcloud)
    plt.axis("off")
    plt.show()
    # fielname = "影评词云图.jpg"
    # with open(fielname,'wt') as f:
    #     f.save(img)

#主函数
main()