到此为止,整个项目的介绍就结束了。由于自己也还是个初学者,接触python不久,代码写的并不好。而且第一次写技术博客,表达的有些冗余,请大家多多包涵,有不对的地方,请大家批评指正。以后我也会将自己做的小项目以这种形式写在博客上和大家一起交流!最后贴上完整的代码。
完整代码
#coding:utf-8
__author__ = 'hang'
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
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)
#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)
wordcloud=wordcloud.fit_words(word_frequence_list)
plt.imshow(wordcloud)
#主函数
main()
结果显示如下:
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