爬取京东评论进行多模态分析的代码
时间: 2024-09-16 07:05:20 浏览: 107
爬取京东商品评论并进行多模态分析涉及到网络爬虫技术和自然语言处理,这里是一个简化的Python示例,使用`requests`库抓取网页数据,`BeautifulSoup`解析HTML,然后利用预训练的多模态模型如BERT或M6进行情感分析:
```python
import requests
from bs4 import BeautifulSoup
from transformers import BertTokenizer, BertForSequenceClassification
# 定义URL和请求头
url = 'https://item.jd.com/<商品ID>.html' # 替换为实际的商品页面链接
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'
}
# 使用BeautifulSoup抓取评论部分
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'lxml')
reviews = soup.find('div', {'class': 'J_Score'}) # 这里假设评论在class为J_Score的元素下
# 对每个评论进行情感分析
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertForSequenceClassification.from_pretrained('bert-base-chinese')
def analyze_sentiment(review):
inputs = tokenizer.encode_plus(review['content'], add_special_tokens=True, return_tensors='pt')
outputs = model(**inputs)
sentiment_score = outputs.logits.softmax(dim=1).detach().numpy()[0][1] # 第二分类,0为负面,1为正面
return sentiment_score
for review in reviews:
content = review['content']
score = analyze_sentiment({'content': content})
print(f'评论内容:{content}, 情感倾向:{score:.2f}(0-1之间,值越接近1表示正面情绪)')
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