{"id":1031754,"date":"2024-12-31T11:28:02","date_gmt":"2024-12-31T03:28:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1031754.html"},"modified":"2024-12-31T11:28:05","modified_gmt":"2024-12-31T03:28:05","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%bf%9b%e8%a1%8c%e6%96%87%e6%9c%ac%e6%83%85%e6%84%9f%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1031754.html","title":{"rendered":"\u5982\u4f55\u7528python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\"<\/p>\n

\u4f7f\u7528Python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6280\u672f\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u3001\u60c5\u611f\u8bcd\u5178\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u3002<\/strong> \u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u5c55\u793a\u5982\u4f55\u901a\u8fc7\u4ee3\u7801\u5b9e\u73b0\u5b83\u4eec\u3002\u5176\u4e2d\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4f7f\u7528NLP\u5e93\u548c\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5982NLTK\u3001TextBlob\u548cVADER\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u9884\u8bad\u7ec3\u6570\u636e\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u548c\u6a21\u578b\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\uff0c\u5c55\u793a\u5b9e\u9645\u4ee3\u7801\u793a\u4f8b\uff0c\u5e76\u89e3\u91ca\u6bcf\u4e00\u6b65\u7684\u539f\u7406\u548c\u4f5c\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6280\u672f<\/h3>\n<\/p>\n

1\u3001NLTK\u5e93<\/h4>\n<\/p>\n

NLTK\uff08Natural Language Toolkit\uff09\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6587\u672c\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u5206\u8bcd\u3001\u6807\u6ce8\u3001\u89e3\u6790\u3001\u8bed\u4e49\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n

\u5b89\u88c5NLTK\u5e93\uff1a<\/strong><\/p>\n<\/p>\n

pip install nltk<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528NLTK\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
import nltk<\/p>\n
from nltk.sentiment import SentimentIntensityAnalyzer<\/p>\n
\u4e0b\u8f7dVADER\u8bcd\u5178<\/strong><\/h2>\n
nltk.download('vader_lexicon')<\/p>\n
\u521b\u5efa\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61<\/strong><\/h2>\n
sia = SentimentIntensityAnalyzer()<\/p>\n
\u5206\u6790\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
sentiment = sia.polarity_scores(text)<\/p>\n
print(sentiment)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4e0b\u8f7d\u4e86VADER\u8bcd\u5178\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61\u3002\u63a5\u7740\uff0c\u6211\u4eec\u5206\u6790\u4e86\u4e00\u6bb5\u6587\u672c\u7684\u60c5\u611f\uff0c\u5e76\u8f93\u51fa\u4e86\u60c5\u611f\u5f97\u5206\u3002VADER\u8bcd\u5178\u662f\u4e00\u79cd\u4e13\u95e8\u7528\u4e8e\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u60c5\u611f\u8bcd\u5178\uff0c\u53ef\u4ee5\u6709\u6548\u5904\u7406\u975e\u6b63\u5f0f\u6587\u672c\u4e2d\u7684\u60c5\u611f\u8868\u8fbe\u3002<\/p>\n<\/p>\n
2\u3001TextBlob\u5e93<\/h4>\n<\/p>\n
TextBlob\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u5b83\u6784\u5efa\u5728NLTK\u548cPattern\u4e4b\u4e0a\uff0c\u63d0\u4f9b\u4e86\u7b80\u6d01\u7684API\uff0c\u4fbf\u4e8e\u8fdb\u884c\u6587\u672c\u5904\u7406\u548c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5b89\u88c5TextBlob\u5e93\uff1a<\/strong><\/p>\n<\/p>\n
pip install textblob<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528TextBlob\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
from textblob import TextBlob<\/p>\n
\u521b\u5efaTextBlob\u5bf9\u8c61<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
blob = TextBlob(text)<\/p>\n
\u5206\u6790\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
sentiment = blob.sentiment<\/p>\n
print(sentiment)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2aTextBlob\u5bf9\u8c61\uff0c\u5e76\u901a\u8fc7\u8c03\u7528sentiment<\/code>\u5c5e\u6027\u5206\u6790\u4e86\u6587\u672c\u7684\u60c5\u611f\u3002TextBlob\u63d0\u4f9b\u7684\u60c5\u611f\u5206\u6790\u529f\u80fd\u76f8\u5bf9\u7b80\u5355\uff0c\u4f46\u5728\u5904\u7406\u4e00\u822c\u6587\u672c\u65f6\u6548\u679c\u826f\u597d\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u9884\u8bad\u7ec3\u6a21\u578b<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528Transformers\u5e93<\/h4>\n<\/p>\n
Transformers\u5e93\u7531Hugging Face\u63d0\u4f9b\uff0c\u5b83\u5305\u542b\u4e86\u5927\u91cf\u9884\u8bad\u7ec3\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u53ef\u4ee5\u7528\u4e8e\u5404\u79cd\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0c\u5305\u62ec\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Transformers\u5e93\uff1a<\/strong><\/p>\n<\/p>\n
pip install transformers<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Transformers\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
from transformers import pipeline<\/p>\n
\u521b\u5efa\u60c5\u611f\u5206\u6790\u7ba1\u9053<\/strong><\/h2>\n
sentiment_analysis = pipeline("sentiment-analysis")<\/p>\n
\u5206\u6790\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
result = sentiment_analysis(text)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Transformers\u5e93\u521b\u5efa\u4e86\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u7ba1\u9053\uff0c\u5e76\u901a\u8fc7\u8c03\u7528\u8be5\u7ba1\u9053\u5206\u6790\u4e86\u6587\u672c\u7684\u60c5\u611f\u3002Transformers\u5e93\u63d0\u4f9b\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u80fd\u591f\u5904\u7406\u66f4\u590d\u6742\u7684\u6587\u672c\u60c5\u611f\u5206\u6790\u4efb\u52a1\uff0c\u6548\u679c\u4f18\u4e8e\u4f20\u7edf\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
2\u3001Fine-tuning BERT\u6a21\u578b<\/h4>\n<\/p>\n
BERT\uff08Bidirectional Encoder Representations from Transformers\uff09\u662f\u8c37\u6b4c\u63d0\u51fa\u7684\u4e00\u79cd\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5df2\u7ecf\u5728\u591a\u4e2a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u663e\u8457\u7684\u6548\u679c\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7Fine-tuning BERT\u6a21\u578b\u6765\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\uff1a<\/strong><\/p>\n<\/p>\n
pip install torch transformers<\/p>\n
<\/code><\/pre>\n<\/p>\n
Fine-tuning BERT\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
import torch<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification<\/p>\n
\u52a0\u8f7d\u9884\u8bad\u7ec3\u7684BERT\u6a21\u578b\u548c\u5206\u8bcd\u5668<\/strong><\/h2>\n
tokenizer = BertTokenizer.from_pretrAI<\/a>ned('bert-base-uncased')<\/p>\n
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')<\/p>\n
\u5206\u8bcd\u5e76\u8f6c\u6362\u4e3a\u5f20\u91cf<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
inputs = tokenizer(text, return_tensors='pt')<\/p>\n
\u8fdb\u884c\u60c5\u611f\u5206\u6790<\/strong><\/h2>\n
with torch.no_grad():<\/p>\n
    outputs = model(inputs)<\/p>\n
    logits = outputs.logits<\/p>\n
    predicted_class = torch.argmax(logits).item()<\/p>\n
print(predicted_class)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u52a0\u8f7d\u4e86\u9884\u8bad\u7ec3\u7684BERT\u6a21\u578b\u548c\u5206\u8bcd\u5668\uff0c\u7136\u540e\u5c06\u6587\u672c\u5206\u8bcd\u5e76\u8f6c\u6362\u4e3a\u5f20\u91cf\u3002\u63a5\u7740\uff0c\u6211\u4eec\u901a\u8fc7\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff0c\u5e76\u8f93\u51fa\u9884\u6d4b\u7684\u60c5\u611f\u7c7b\u522b\u3002Fine-tuning BERT\u6a21\u578b\u80fd\u591f\u5904\u7406\u66f4\u590d\u6742\u7684\u6587\u672c\u60c5\u611f\u5206\u6790\u4efb\u52a1\uff0c\u4f46\u9700\u8981\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u8bad\u7ec3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u60c5\u611f\u8bcd\u5178<\/h3>\n<\/p>\n
1\u3001VADER\u60c5\u611f\u8bcd\u5178<\/h4>\n<\/p>\n
VADER\uff08Valence Aware Dictionary and sEntiment Reasoner\uff09\u662f\u4e00\u79cd\u4e13\u95e8\u7528\u4e8e\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u60c5\u611f\u8bcd\u5178\uff0c\u80fd\u591f\u5904\u7406\u975e\u6b63\u5f0f\u6587\u672c\u4e2d\u7684\u60c5\u611f\u8868\u8fbe\u3002<\/p>\n<\/p>\n
\u4f7f\u7528VADER\u60c5\u611f\u8bcd\u5178\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
import nltk<\/p>\n
from nltk.sentiment import SentimentIntensityAnalyzer<\/p>\n
\u4e0b\u8f7dVADER\u8bcd\u5178<\/strong><\/h2>\n
nltk.download('vader_lexicon')<\/p>\n
\u521b\u5efa\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61<\/strong><\/h2>\n
sia = SentimentIntensityAnalyzer()<\/p>\n
\u5206\u6790\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
sentiment = sia.polarity_scores(text)<\/p>\n
print(sentiment)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4e0b\u8f7d\u4e86VADER\u8bcd\u5178\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61\u3002\u63a5\u7740\uff0c\u6211\u4eec\u5206\u6790\u4e86\u4e00\u6bb5\u6587\u672c\u7684\u60c5\u611f\uff0c\u5e76\u8f93\u51fa\u4e86\u60c5\u611f\u5f97\u5206\u3002VADER\u8bcd\u5178\u662f\u4e00\u79cd\u4e13\u95e8\u7528\u4e8e\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u60c5\u611f\u8bcd\u5178\uff0c\u53ef\u4ee5\u6709\u6548\u5904\u7406\u975e\u6b63\u5f0f\u6587\u672c\u4e2d\u7684\u60c5\u611f\u8868\u8fbe\u3002<\/p>\n<\/p>\n
2\u3001AFINN\u60c5\u611f\u8bcd\u5178<\/h4>\n<\/p>\n
AFINN\u662f\u4e00\u79cd\u57fa\u4e8e\u60c5\u611f\u8bcd\u5178\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u8ba1\u7b97\u6587\u672c\u4e2d\u60c5\u611f\u8bcd\u6c47\u7684\u5f97\u5206\u6765\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5b89\u88c5AFINN\u5e93\uff1a<\/strong><\/p>\n<\/p>\n
pip install afinn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528AFINN\u60c5\u611f\u8bcd\u5178\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
from afinn import Afinn<\/p>\n
\u521b\u5efaAFINN\u5bf9\u8c61<\/strong><\/h2>\n
afinn = Afinn()<\/p>\n
\u5206\u6790\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
text = "I love this movie, it's fantastic!"<\/p>\n
sentiment = afinn.score(text)<\/p>\n
print(sentiment)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2aAFINN\u5bf9\u8c61\uff0c\u5e76\u901a\u8fc7\u8c03\u7528score<\/code>\u65b9\u6cd5\u5206\u6790\u4e86\u6587\u672c\u7684\u60c5\u611f\u3002AFINN\u60c5\u611f\u8bcd\u5178\u65b9\u6cd5\u76f8\u5bf9\u7b80\u5355\uff0c\u4f46\u5728\u5904\u7406\u4e00\u822c\u6587\u672c\u65f6\u6548\u679c\u826f\u597d\u3002<\/p>\n<\/p>\n
\u56db\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528Scikit-Learn\u8fdb\u884c\u60c5\u611f\u5206\u6790<\/h4>\n<\/p>\n
Scikit-Learn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7b97\u6cd5\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u4e8e\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u5305\u62ec\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Scikit-Learn\u5e93\uff1a<\/strong><\/p>\n<\/p>\n
pip install scikit-learn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Scikit-Learn\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/strong><\/p>\n<\/p>\n
from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
from sklearn.naive_bayes import MultinomialNB<\/p>\n
from sklearn.pipeline import make_pipeline<\/p>\n
\u521b\u5efa\u6587\u672c\u548c\u6807\u7b7e\u6570\u636e<\/strong><\/h2>\n
texts = ["I love this movie, it's fantastic!", "I hate this movie, it's terrible!"]<\/p>\n
labels = [1, 0]<\/p>\n
\u521b\u5efa\u6587\u672c\u5411\u91cf\u5316\u5668\u548c\u5206\u7c7b\u5668<\/strong><\/h2>\n
vectorizer = CountVectorizer()<\/p>\n
classifier = MultinomialNB()<\/p>\n
\u521b\u5efa\u7ba1\u9053<\/strong><\/h2>\n
model = make_pipeline(vectorizer, classifier)<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(texts, labels)<\/p>\n
\u5206\u6790\u65b0\u6587\u672c\u60c5\u611f<\/strong><\/h2>\n
new_text = "I love this movie, it's fantastic!"<\/p>\n
predicted_label = model.predict([new_text])<\/p>\n
print(predicted_label)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u6587\u672c\u548c\u6807\u7b7e\u6570\u636e\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u6587\u672c\u5411\u91cf\u5316\u5668\u548c\u5206\u7c7b\u5668\uff0c\u5e76\u5c06\u5b83\u4eec\u7ec4\u5408\u6210\u4e00\u4e2a\u7ba1\u9053\u3002\u63a5\u7740\uff0c\u6211\u4eec\u8bad\u7ec3\u4e86\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5206\u6790\u4e86\u65b0\u6587\u672c\u7684\u60c5\u611f\u3002\u4f7f\u7528Scikit-Learn\u8fdb\u884c\u60c5\u611f\u5206\u6790\u65b9\u6cd5\u7b80\u5355\u3001\u6613\u7528\uff0c\u4f46\u9700\u8981\u4e00\u5b9a\u7684\u8bad\u7ec3\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9e\u6218\u6848\u4f8b<\/h3>\n<\/p>\n
1\u3001\u5206\u6790\u793e\u4ea4\u5a92\u4f53\u60c5\u611f<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e0a\u8ff0\u65b9\u6cd5\u5206\u6790\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u60c5\u611f\uff0c\u4ee5\u4e86\u89e3\u516c\u4f17\u5bf9\u67d0\u4e2a\u8bdd\u9898\u7684\u6001\u5ea6\u3002<\/p>\n<\/p>\n
\u793a\u4f8b\u4ee3\u7801\uff1a<\/strong><\/p>\n<\/p>\n
import tweepy<\/p>\n
from nltk.sentiment import SentimentIntensityAnalyzer<\/p>\n
\u8bbe\u7f6eTwitter API\u5bc6\u94a5<\/strong><\/h2>\n
api_key = "your_api_key"<\/p>\n
api_secret_key = "your_api_secret_key"<\/p>\n
access_token = "your_access_token"<\/p>\n
access_token_secret = "your_access_token_secret"<\/p>\n
\u8ba4\u8bc1Twitter API<\/strong><\/h2>\n
auth = tweepy.OAuth1UserHandler(api_key, api_secret_key, access_token, access_token_secret)<\/p>\n
api = tweepy.API(auth)<\/p>\n
\u83b7\u53d6\u63a8\u6587<\/strong><\/h2>\n
tweets = api.search(q="Python", lang="en", count=100)<\/p>\n
\u521b\u5efa\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61<\/strong><\/h2>\n
sia = SentimentIntensityAnalyzer()<\/p>\n
\u5206\u6790\u63a8\u6587\u60c5\u611f<\/strong><\/h2>\n
for tweet in tweets:<\/p>\n
    text = tweet.text<\/p>\n
    sentiment = sia.polarity_scores(text)<\/p>\n
    print(text)<\/p>\n
    print(sentiment)<\/p>\n
    print()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8bbe\u7f6e\u4e86Twitter API\u5bc6\u94a5\uff0c\u5e76\u8ba4\u8bc1\u4e86Twitter API\u3002\u63a5\u7740\uff0c\u6211\u4eec\u83b7\u53d6\u4e86\u4e0e\u201cPython\u201d\u76f8\u5173\u7684\u63a8\u6587\uff0c\u5e76\u4f7f\u7528VADER\u60c5\u611f\u8bcd\u5178\u5206\u6790\u4e86\u63a8\u6587\u7684\u60c5\u611f\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4e86\u89e3\u516c\u4f17\u5bf9\u67d0\u4e2a\u8bdd\u9898\u7684\u6001\u5ea6\u3002<\/p>\n<\/p>\n
2\u3001\u6784\u5efa\u60c5\u611f\u5206\u6790\u5e94\u7528<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e0a\u8ff0\u65b9\u6cd5\u6784\u5efa\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u5e94\u7528\uff0c\u4ee5\u4fbf\u5bf9\u7528\u6237\u8f93\u5165\u7684\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u793a\u4f8b\u4ee3\u7801\uff1a<\/strong><\/p>\n<\/p>\n
from flask import Flask, request, jsonify<\/p>\n
from nltk.sentiment import SentimentIntensityAnalyzer<\/p>\n
\u521b\u5efaFlask\u5e94\u7528<\/strong><\/h2>\n
app = Flask(__name__)<\/p>\n
\u521b\u5efa\u60c5\u611f\u5206\u6790\u5668\u5bf9\u8c61<\/strong><\/h2>\n
sia = SentimentIntensityAnalyzer()<\/p>\n
\u5b9a\u4e49\u60c5\u611f\u5206\u6790\u8def\u7531<\/strong><\/h2>\n
@app.route('\/analyze', methods=['POST'])<\/p>\n
def analyze():<\/p>\n
    text = request.json['text']<\/p>\n
    sentiment = sia.polarity_scores(text)<\/p>\n
    return jsonify(sentiment)<\/p>\n
\u542f\u52a8\u5e94\u7528<\/strong><\/h2>\n
if __name__ == '__main__':<\/p>\n
    app.run()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Flask\u6846\u67b6\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684Web\u5e94\u7528\uff0c\u5e76\u5b9a\u4e49\u4e86\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u8def\u7531\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7POST\u8bf7\u6c42\u53d1\u9001\u6587\u672c\u5230\u8be5\u8def\u7531\uff0c\u5e94\u7528\u4f1a\u8fd4\u56de\u6587\u672c\u7684\u60c5\u611f\u5f97\u5206\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u5e94\u7528\uff0c\u4ee5\u4fbf\u5bf9\u7528\u6237\u8f93\u5165\u7684\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<\/p>\n
\u7ed3\u8bba<\/h3>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u63a2\u8ba8\u4e86\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\uff0c\u4ecb\u7ecd\u4e86\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u3001\u60c5\u611f\u8bcd\u5178\u548c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u7f3a\u70b9\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u60c5\u611f\u5206\u6790\u4efb\u52a1\u3002\u901a\u8fc7\u5b9e\u9645\u4ee3\u7801\u793a\u4f8b\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\uff0c\u5e76\u63a2\u8ba8\u4e86\u5206\u6790\u793e\u4ea4\u5a92\u4f53\u60c5\u611f\u548c\u6784\u5efa\u60c5\u611f\u5206\u6790\u5e94\u7528\u7684\u5b9e\u6218\u6848\u4f8b\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u8bfb\u8005\u638c\u63e1\u4f7f\u7528Python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\u7684\u6280\u5de7\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\uff1f<\/strong>
\u5728\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u975e\u5e38\u5173\u952e\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecNLTK\u3001TextBlob\u3001VADER\u548cTransformers\u7b49\u3002NLTK\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\uff0c\u9002\u5408\u57fa\u7840\u5206\u6790\uff1bTextBlob\u5219\u76f8\u5bf9\u7b80\u5355\uff0c\u9002\u5408\u5feb\u901f\u539f\u578b\u5f00\u53d1\uff1bVADER\u4e13\u6ce8\u4e8e\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u60c5\u611f\u5206\u6790\uff1b\u800cTransformers\u5e93\u80fd\u591f\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u66f4\u590d\u6742\u7684\u60c5\u611f\u5206\u6790\uff0c\u9002\u7528\u4e8e\u9700\u8981\u9ad8\u51c6\u786e\u5ea6\u7684\u573a\u666f\u3002\u6839\u636e\u9879\u76ee\u9700\u6c42\u548c\u6570\u636e\u7c7b\u578b\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u63d0\u9ad8\u5206\u6790\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n
\u6587\u672c\u9884\u5904\u7406\u5728\u60c5\u611f\u5206\u6790\u4e2d\u6709\u591a\u91cd\u8981\uff1f<\/strong>
\u6587\u672c\u9884\u5904\u7406\u662f\u8fdb\u884c\u60c5\u611f\u5206\u6790\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u80fd\u591f\u663e\u8457\u63d0\u5347\u6a21\u578b\u7684\u6548\u679c\u3002\u5e38\u89c1\u7684\u9884\u5904\u7406\u8fc7\u7a0b\u5305\u62ec\u53bb\u9664\u6807\u70b9\u7b26\u53f7\u3001\u8f6c\u6362\u4e3a\u5c0f\u5199\u3001\u53bb\u9664\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\u548c\u8bcd\u5f62\u8fd8\u539f\u7b49\u3002\u8fd9\u4e9b\u64cd\u4f5c\u53ef\u4ee5\u5e2e\u52a9\u51cf\u5c11\u566a\u58f0\uff0c\u7a81\u51fa\u6587\u672c\u4e2d\u7684\u5173\u952e\u4fe1\u606f\u3002\u540c\u65f6\uff0c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u524d\u7684\u9002\u5f53\u9884\u5904\u7406\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u7387\uff0c\u964d\u4f4e\u8fc7\u62df\u5408\u7684\u98ce\u9669\uff0c\u786e\u4fdd\u6a21\u578b\u5728\u672a\u89c1\u6570\u636e\u4e0a\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n
\u5982\u4f55\u8bc4\u4f30\u6587\u672c\u60c5\u611f\u5206\u6790\u7684\u6548\u679c\uff1f<\/strong>
\u8bc4\u4f30\u6587\u672c\u60c5\u611f\u5206\u6790\u7684\u6548\u679c\u901a\u5e38\u4f9d\u8d56\u4e8e\u591a\u79cd\u6307\u6807\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ec\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1-score\u7b49\u3002\u51c6\u786e\u7387\u662f\u6b63\u786e\u5206\u7c7b\u7684\u6bd4\u4f8b\uff0c\u53ec\u56de\u7387\u5219\u5173\u6ce8\u6a21\u578b\u8bc6\u522b\u6b63\u9762\u548c\u8d1f\u9762\u60c5\u611f\u7684\u80fd\u529b\uff0c\u800cF1-score\u5219\u662f\u51c6\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u8c03\u548c\u5e73\u5747\u6570\uff0c\u80fd\u591f\u66f4\u5168\u9762\u5730\u53cd\u6620\u6a21\u578b\u8868\u73b0\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u6765\u53ef\u89c6\u5316\u6a21\u578b\u7684\u5206\u7c7b\u7ed3\u679c\uff0c\u4ece\u800c\u66f4\u6df1\u5165\u5730\u5206\u6790\u6a21\u578b\u7684\u4f18\u7f3a\u70b9\u3002\u4f7f\u7528\u8fd9\u4e9b\u8bc4\u4f30\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4e0d\u65ad\u4f18\u5316\u60c5\u611f\u5206\u6790\u6a21\u578b\uff0c\u63d0\u9ad8\u5176\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u8fdb\u884c\u6587\u672c\u60c5\u611f\u5206\u6790\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6280\u672f\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u3001\u60c5\u611f\u8bcd\u5178\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002 […]","protected":false},"author":3,"featured_media":1031763,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1031754"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1031754"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1031754\/revisions"}],"predecessor-version":[{"id":1031767,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1031754\/revisions\/1031767"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1031763"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1031754"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1031754"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1031754"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}