《攀登者》影评爬取及分析
0、项目结构
其中simkai.ttf为字体文件,Windows查看系统自带的字体
C:\Windows\Fonts
一、爬取豆瓣影评数据
# -*- coding: utf-8 -*-
"""爬取豆瓣影评"""
import requests
from lxml import etree
import time
url = "https://movie.douban.com/subject/30413052/comments?start=%d&limit=20&sort=new_score&status=P"
#请求头
headers = {'Host': 'movie.douban.com',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:70.0) Gecko/20100101 Firefox/70.0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',
#'Accept-Encoding': 'gzip, deflate, br',
'Connection': 'keep-alive',
'Cookie': 'bid=TXwfIvNFTRE; douban-fav-remind=1; __gads=ID=e042951d078c30b3:T=1570518321:S=ALNI_Mbp-ZmoryuBFEnTQy24mwdf0B89ig; __utma=30149280.1448315194.1570518324.1570518324.1572927825.2; __utmz=30149280.1570518324.1.1.utmcsr=baidu|utmccn=(organic)|utmcmd=organic; _pk_id.100001.4cf6=589509e524ead00f.1572927824.1.1572927824.1572927824.; _pk_ses.100001.4cf6=*; __utmb=30149280.0.10.1572927825; __utmc=30149280; __utma=223695111.1094105223.1572927825.1572927825.1572927825.1; __utmb=223695111.0.10.1572927825; __utmc=223695111; __utmz=223695111.1572927825.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); ap_v=0,6.0',
'Upgrade-Insecure-Requests': '1',
'Cache-Control': 'max-age=0'}
if __name__ == '__main__':
f = open("./climb.csv", mode="w", encoding='utf-8')
f.write("author\tcomment\tvotes\n")
#start:0,20,40,...,200
for i in range(11):#range左闭右开
#1拼接url,只能获取前11页数据
if i == 10:#最后一页
url_climb = url%(200)
else:
url_climb = url%(i*20)
#2发起请求,设置编码,获取文本内容
response = requests.get(url_climb, headers = headers)
response.encoding = "utf-8"
text = response.text
#存储
#with open("./climb.html", mode="w", encoding="utf-8") as f:
# f.write(text)
#使用etree解析
html = etree.HTML(text)
comments = html.xpath('//div[@id="comments"]/div[@class="comment-item"]')
for comment in comments:
#获取评论人
author = comment.xpath('./div[@class="avatar"]/a/@title')[0].strip()
#获取评论内容
p = comment.xpath('.//span[@class="short"]/text()')[0].strip()
#获取这条评论对应的点赞数
vote = comment.xpath('.//span[@class="votes"]/text()')[0].strip()
#print(author, p, vote)
f.write("%s\t%s\t%s\n" % (author,p,vote))
#打印提示信息,并休眠一秒,反爬虫
print("第%d页的数据保存成功" % (i+1))
time.sleep(1)
f.close()
二、对评论信息进行情感分析
# -*- coding: utf-8 -*-
"""
pandas:python data analysis lib,返回值为DataFrame(行,列),行是样本,列为属性
"""
import pandas as pd
from snownlp import SnowNLP
# 显示所有列
pd.set_option('display.max_columns', None)
def convert(comment):
"""将传入的评论进行情感分析"""
snow = SnowNLP(str(comment))
sentiments = snow.sentiments#0(消极评论)-1(积极评论)
return sentiments
if __name__ =='__main__':
data = pd.read_csv('./climb.csv', '\t')
#print(data.head(), "\n", data.shape)
#获取评论数据,进行情感分析,DataFrame就会新增加一列名为‘情感评分’的数据
data['情感评分'] = data.comment.apply(convert)
data.sort_values(by='情感评分', ascending=False, inplace=True)
#保存数据
data.to_csv('./climb_snownlp.csv', sep='\t', index=False, encoding='utf-8')
print(data[:5])
print(data[-5:])
三、对评论数据进行jieba分词,生成关键词条形图和词云
# -*- coding: utf-8 -*-
import pandas as pd
import jieba
from jieba import analyse
import matplotlib.pyplot as plt
import numpy as np
import wordcloud
from PIL import Image
if __name__ == '__main__':
data = pd.read_csv('./climb.csv', sep='\t')
#列表生成式,获取所有评论信息
comments = ';'.join([str(c) for c in data['comment'].tolist()])
#print(comments)
#使用jieba库对文本进行分词,返回的是生成器
gen_ret = jieba.cut(comments)
seg_words = '/'.join(gen_ret)
#print(seg_words)
#对分好的词进行分析,topK返回的关键词个数,withWeight带着权重
tags_ret = analyse.extract_tags(seg_words, topK=500, withWeight=True)
#print(tags_ret)
#将数据转换成DataFrame
df_ret = pd.DataFrame(tags_ret, columns=['词语', '重要性'])
df_ret.sort_values(by='重要性', ascending=False, inplace=True)#根据重要性降序排列
#print(df_ret)
#可视化,500个词语,选取前20个分析
plt.barh(y=np.arange(0,20), width=df_ret[:20]['重要性'][::-1])
plt.ylabel('Importance')
plt.yticks(np.arange(0,20), labels=df_ret[:20]['词语'][::-1], fontproperties='KaiTi')
#保存条形图!!!保存代码一定要写在show之前,dpi表示屏幕像素密度
plt.savefig('./条形图_20个keyword.jpg', dpi=200)
plt.show()
#词云操作
bg = np.array(Image.open('./bg.jpg'))#词云的图片
words = dict(tags_ret)#将标签转为词典
cloud = wordcloud.WordCloud(width=1200, height=968,
font_path='./simkai.ttf',#字体路径
background_color='white', mask=bg,
max_words=500, max_font_size=150)
#生成词云图片
word_cloud = cloud.generate_from_frequencies(words)
plt.figure(figsize=(12,12))
plt.imshow(word_cloud)
#词云保存
plt.savefig('./攀登者词云.jpg', dpi=200)
plt.show()