数据分析-02 复联4短评分析

还是大鹏老师课程的复习，这节课是对复联4的短评进行爬取与分析

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1、数据爬取 (函数式编程，requests包和BeautifulSoup包的使用)

# 导入工具包
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 获取urls(观察url的规律)
def get_urls(n):
    urllst = []
    for i in range(n):
        urllst.append('https://movie.douban.com/subject/26100958/comments?start=%i&limit=20&sort=new_score&status=P' % (i*20))
    return urllst

urllsts = get_urls(50)

# 获取网页数据
def get_data(urli):
    try:
        ri = requests.get(url = u)
        ri.encoding = 'utf-8'
        soupi = BeautifulSoup(ri.text)
        infor_lst = soupi.find('div',id="comments").find_all('div',class_="comment-item")
         
        datalsti = []
        for infor in infor_lst[:]:
            dic = {}
            dic['评论者'] = infor.find('span',class_="comment-info").find('a').text
            dic['评分'] = int(infor.find('span',class_="comment-info").find_all('span')[1]['class'][0][-2:])
            dic['评论时间'] = infor.find('span',class_="comment-time").text.replace(' ','').replace('\n','')
            dic['有用数量'] = int(infor.find('span',class_="votes").text)
            dic['评论内容'] = infor.find('p').text.replace('\n','')
            datalsti.append(dic)
        return datalsti
    except:
        return []

# 批量获取数据
datalst = []
n = 1
for u in urllsts:
    datalst.extend(get_data(u))
    print('成功获取%i条数据' % (n*20))
    n += 1

df = pd.DataFrame(datalst)

image.png

2、数据查看 (直方图和散点图的使用)

# 评论字数数据分布
df['评论字数'] = df['评论内容'].str.len()

plt.figure(figsize = (12,5))
plt.title('评论字数数据分布')
df['评论字数'].hist(bins = 20,edgecolor = 'white')
plt.grid(linestyle='--')

# 评论字数与有用数量关系
plt.figure(figsize = (12,5))
plt.title('评论字数与有用数量关系')
plt.scatter(df['评论字数'],df['有用数量'],alpha = 0.4)
plt.xlabel('评论字数')
plt.ylabel('有用数量')
plt.grid(linestyle='--')

df[df['有用数量']>5000].sort_values(by = '有用数量',ascending = False)

3、关键人物分析 (人物出现次数)

# 创建函数查找美队出现次数
def name_count(namei,s):
    n = 0
    for i in s:
        if namei in i:
            n += 1
        else:
            continue
    return n

name_count('美队',df['评论内容'])

# 计算不同关键字出现频率
namelst = ['美队','钢铁侠','灭霸','黑寡妇','雷神','浩克','惊奇队长',
            '鹰眼','蚁人','奇异博士','蜘蛛侠','星云','黑豹']

lst = []
for namei in namelst:
    lst.append({'关键词': namei,'出现频率': name_count(namei,df['评论内容'])})

# 保存数据
result = pd.DataFrame(lst)
result.to_csv('/home/kesci/result.csv')