理解你的需求后，我们将分两部分来完成任务： 1. **第一部分**：在一个完整的Python脚本中实现前6条要求，包括爬取图书网站的书本信息并进行情感分析。 2. **第二部分**：创建一个新的Scrapy项目，并基于Scrapy框架编写爬虫，以提取商品名称、价格和评价。 ### 第一部分：前6条要求的完整Python脚本 ```python # 导入必要的库 import requests from bs4 import BeautifulSoup import re from lxml import etree from selenium import webdriver from selenium.webdriver.chrome.options import Options from aip import AipNlp # 百度AI平台参数 APP_ID = 'your_app_id' API_KEY = 'your_api_key' SECRET_KEY = 'your_secret_key' client = AipNlp(APP_ID, API_KEY, SECRET_KEY) # 爬取网页内容 def fetch_web_content(url): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} response = requests.get(url, headers=headers) response.encoding = 'utf-8' return response.text # 解析网页并提取信息 def parse_and_extract(content): soup = BeautifulSoup(content, 'html.parser') books = soup.find_all('li', class_='gl-item') data = [] for book in books: try: name = book.find('div', class_='p-name').get_text(strip=True) price = book.find('i').get_text(strip=True) data.append({'name': name, 'price': price}) except AttributeError: continue return data # 清洗提取的信息 def clean_data(data): cleaned_data = [] for item in data: cleaned_name = re.sub(r'\s+', ' ', item['name']) cleaned_price = re.sub(r'[^0-9.]', '', item['price']) cleaned_data.append({'name': cleaned_name, 'price': cleaned_price}) return cleaned_data # 使用XPath提取详情链接 def extract_detail_links(content): tree = etree.HTML(content) detail_links = tree.xpath('//div[@class="p-img"]/a/@href') full_detail_links = ['https:' + link for link in detail_links] # 将相对路径转换为绝对路径 return full_detail_links # 使用Selenium获取登录后才能访问的评论信息 def get_reviews_with_selenium(links): chrome_options = Options() chrome_options.add_argument('--headless') # 设置无头模式 browser = webdriver.Chrome(options=chrome_options) # 登录过程省略，实际操作中需要先登录账号 reviews = [] for link in links[:1]: # 仅作为演示，限制只访问一个详情页 browser.get(link) comments_section = browser.find_elements_by_class_name('comment-item') for comment in comments_section: try: review = comment.find_element_by_class_name('comment-con').text reviews.append(review) except Exception as e: print(e) browser.quit() return reviews # 情感分析 def analyze_sentiment(reviews): results = [] for review in reviews: result = client.sentimentClassify(review) sentiment = '正面' if result['items'][0]['positive_prob'] > 0.5 else '负面' results.append({'review': review, 'sentiment': sentiment}) return results if __name__ == "__main__": # 京东图书分类页面URL url = 'https://book.jd.com/booksort.html' # 爬取网页内容 content = fetch_web_content(url) # 解析并提取书籍信息 data = parse_and_extract(content) # 清洗提取的信息 cleaned_data = clean_data(data) # 提取详情链接 detail_links = extract_detail_links(content) # 使用Selenium获取评论信息（可选部分） reviews = get_reviews_with_selenium(detail_links) # 情感分析 sentiments = analyze_sentiment(reviews) for sentiment in sentiments: print(f"评价: {sentiment['review']}, 情感倾向: {sentiment['sentiment']}") ``` ### 第二部分：创建新的Scrapy项目并编写框架 #### 创建Scrapy项目 在终端中执行以下命令创建一个新的Scrapy项目： ```bash scrapy startproject jd_books cd jd_books ``` #### 编写爬虫框架 编辑`jd_books/spiders/jd_book_spider.py`： ```python import scrapy from ..items import JdBooksItem class JdBookSpider(scrapy.Spider): name = 'jd_book_spider' allowed_domains = ['jd.com'] start_urls = ['https://book.jd.com/booksort.html'] def parse(self, response): # 使用BeautifulSoup解析HTML soup = BeautifulSoup(response.text, 'lxml') books = soup.find_all('li', class_='gl-item') for book in books: try: item = JdBooksItem() item['name'] = book.find('div', class_='p-name').get_text(strip=True) item['price'] = book.find('i').get_text(strip=True) # 提取详情链接 detail_link = 'https:' + book.find('div', class_='p-name').a['href'] yield scrapy.Request(detail_link, callback=self.parse_detail, meta={'item': item}) except AttributeError: continue def parse_detail(self, response): item = response.meta['item'] tree = etree.HTML(response.text) reviews_section = tree.xpath('//div[@id="comment-content"]//div[@class="comment-item"]') reviews = [] for review_section in reviews_section: review = ''.join(review_section.xpath('.//div[@class="comment-con"]/text()')).strip() if review: reviews.append(review) item['review'] = reviews yield item ``` #### 编辑`items.py` ```python import scrapy class JdBooksItem(scrapy.Item): name = scrapy.Field() price = scrapy.Field() review = scrapy.Field() ``` #### 编辑`pipelines.py` ```python import re class JdBooksPipeline: def process_item(self, item, spider): # 数据清洗 item['name'] = re.sub(r'\s+', ' ', item['name']) item['price'] = re.sub(r'[^0-9.]', '', item['price']) return item ``` #### 编辑`settings.py` ```python # 设置User-Agent USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3' # 开启Pipeline ITEM_PIPELINES = { 'jd_books.pipelines.JdBooksPipeline': 300, } # 下载延迟 DOWNLOAD_DELAY = 2 ``` ### 知识点 1. **HTTP请求与响应**：通过发送HTTP GET请求来获取远程服务器的数据。 2. **HTML/XML解析**：运用BeautifulSoup或lxml解析HTML/XML文档，从中抽取所需信息。 3. **情感分析**：利用百度AI平台的情感分析API判断文本的情感倾向。 ### 总结 1. **第一部分**的脚本实现了前六条要求，整合了requests、BeautifulSoup、re、lxml、selenium和baidu_aip库。 2. **第二部分**展示了如何创建一个Scrapy项目，并编写爬虫框架以提取商品名称、价格和评价。 希望这能满足你的需求。如果有任何进一步的问题或调整，请随时告知！