python scrapy.爬取豆瓣某电影影评并进行数据分析

好的,我来为您介绍一下如何使用Python的Scrapy框架爬取豆瓣某电影的影评并进行数据分析: 1. 环境搭建: 首先需要安装Scrapy库,可以使用pip命令: ``` pip install scrapy ``` 2. 创建Scrapy项目: ``` scrapy startproject douban_comments cd douban_comments ``` 3. 编写爬虫: 在spiders目录下创建一个新的爬虫文件,例如douban_spider.py: ```python import scrapy from douban_comments.items import DoubanCommentsItem class DoubanSpider(scrapy.Spider): name = "douban" allowed_domains = ["movie.douban.com"] start_urls = ["https://movie.douban.com/subject/1292052/comments?status=P"] def parse(self, response): comments = response.xpath('//div[@class="comment"]') for comment in comments: item = DoubanCommentsItem() item['user'] = comment.xpath('.//span[@class="comment-info"]/a/text()').get() item['rating'] = comment.xpath('.//span[@class="comment-info"]/span[2]/@title').get() item['content'] = comment.xpath('.//span[@class="short"]/text()').get() item['time'] = comment.xpath('.//span[@class="comment-time "]/@title').get() yield item # 翻页 next_page = response.xpath('//span[@class="next"]/a/@href').get() if next_page: yield scrapy.Request(url=response.urljoin(next_page), callback=self.parse) ``` 4. 定义Item: 在items.py中定义要爬取的数据结构: ```python import scrapy class DoubanCommentsItem(scrapy.Item): user = scrapy.Field() rating = scrapy.Field() content = scrapy.Field() time = scrapy.Field() ``` 5. 运行爬虫: ``` scrapy crawl douban -o comments.json ``` 6. 数据分析: 爬取完成后,可以使用pandas进行数据分析: ```python import pandas as pd import matplotlib.pyplot as plt # 读取数据 df = pd.read_json('comments.json') # 清洗数据 df['time'] = pd.to_datetime(df['time']) df['rating'] = df['rating'].str.extract('(\d+)').astype(int) # 分析评分分布 rating_counts = df['rating'].value_counts().sort_index() rating_counts.plot(kind='bar') plt.title('评分分布') plt.xlabel('评分') plt.ylabel('数量') plt.show() # 分析评论趋势 df.set_index('time').resample('D').size().plot() plt.title('评论数量趋势') plt.xlabel('日期') plt.ylabel('数量') plt.show() ``` 通过以上步骤,我们可以爬取豆瓣某电影的影评数据,并进行简单的数据分析。这个过程可以帮助我们了解电影的口碑变化趋势,以及观众对电影的总体评价。