python大作业

词云---利用python对电影评价的爬取

一、抓取网页数据

1：网页爬取一些数据的前期工作

from urllib import request
resp = request.urlopen('https://movie.douban.com/nowplaying/hangzhou/')
html_data = resp.read().decode('utf-8')

:2：爬取得到的html解析

from bs4 import BeautifulSoup as bs 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')

在上图中可以看到data-subject属性里面id，而在img标签的电影的名字，两个属性来获得电影的id和名称。

nowplaying_list = [] 
for i in nowplaying_movie_list:        
        nowplaying_dict = {}        
        nowplaying_dict['id'] = i['data-subject']       
        for tag_img_item in i.find_all('img'):            
            nowplaying_dict['name'] = tag_img_item['alt']            
            nowplaying_list.append(nowplaying_dict)

二、数据的处理

comments = ''
for k in range(len(eachCommentList)):
    comments = comments + (str(eachCommentList[k])).strip()

三、词云生成图片

import matplotlib.pyplot as plt
%matplotlib inline

import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)
from wordcloud import WordCloud#词云包

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)

wordcloud=wordcloud.fit_words(word_frequence_list)
plt.imshow(wordcloud)

付源码

# -*- coding: utf-8 -*-

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 PIL import Image
from urllib import request
from bs4 import BeautifulSoup as bs
from wordcloud import WordCloud,ImageColorGenerator # 词云包
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)



# 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="	", 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)
    #  print(words_stat.head())

    bg_pic = numpy.array(Image.open("alice_mask.png"))

    # 用词云进行显示
    wordcloud = WordCloud(
        font_path="simhei.ttf",
        background_color="white",
        max_font_size=80,
        width = 2000,
        height = 1800,
        mask = bg_pic,
        mode = "RGBA"
    )
    word_frequence = {x[0]: x[1] for x in words_stat.head(1000).values}
    # print(word_frequence)
    """
    word_frequence_list = []
    for key in word_frequence:
        temp = (key, word_frequence[key])
        word_frequence_list.append(temp)
        #print(word_frequence_list)
    """
    wordcloud = wordcloud.fit_words(word_frequence)

    image_colors = ImageColorGenerator(bg_pic) # 根据图片生成词云颜色

    plt.imshow(wordcloud) #显示词云图片
    plt.axis("off")
    plt.show()
    wordcloud.to_file('show_Chinese.png')  # 把词云保存下来

main()