File size: 9,811 Bytes
f5b7e31 eabeab3 f5b7e31 0f2373c f5b7e31 86fdee3 f5b7e31 86fdee3 f5b7e31 86fdee3 f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 86fdee3 0f2373c f5b7e31 0f2373c f5b7e31 0f2373c f5b7e31 eabeab3 f5b7e31 eabeab3 f5b7e31 eabeab3 f5b7e31 0f2373c f5b7e31 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 |
"""
AI News API Handler
Fetches AI-related news from NewsAPI and performs sentiment analysis
"""
import requests
import pandas as pd
from datetime import datetime, timedelta
import os
import json
from dotenv import load_dotenv
from textblob import TextBlob
from typing import List, Dict, Optional
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as SIA
# Load environment variables
load_dotenv()
class AINewsAnalyzer:
def __init__(self):
self.api_key = os.getenv('NEWSAPI_KEY')
self.base_url = "https://newsapi.org/v2/everything"
if not self.api_key:
raise ValueError("NewsAPI key not found. Please set NEWSAPI_KEY in your .env file")
def fetch_ai_news(self,
query: str = "artificial intelligence",
days: tuple[int] = (7,14),
language: str = "en",
sources: Optional[str] = None,
page_size: int = 100) -> List[Dict]:
"""
Fetch AI-related news from NewsAPI
Args:
query: Search query for news articles
days: Number of days to look back
language: Language code (default: "en")
sources: Comma-separated string of news sources
page_size: Number of articles to fetch (max 100)
Returns:
List of news articles with metadata
"""
# Calculate date range
today = datetime.now()
from_date = today - timedelta(days=days[0]) # 7
to_date = today - timedelta(days=days[1]) # 14
print(from_date, to_date)
# Prepare API parameters
params = {
'q': query,
'from': from_date.strftime('%Y-%m-%d'),
'to': to_date.strftime('%Y-%m-%d'),
'language': language,
'sortBy': 'publishedAt',
'pageSize': page_size,
'apiKey': self.api_key
}
# Add sources if specified
if sources:
params['sources'] = sources
try:
# Make API request
response = requests.get(self.base_url, params=params)
response.raise_for_status()
data = response.json()
if data['status'] == 'ok':
return data['articles']
else:
print(f"API Error: {data.get('message', 'Unknown error')}")
return []
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
return []
def analyze_sentiment(self, text: str, model: str) -> Dict:
"""
Analyze sentiment of given text using TextBlob
Args:
text: Text to analyze
Returns:
Dictionary with sentiment metrics
"""
if not text:
return {
'polarity': 0.0,
'subjectivity': 0.0,
'label': 'neutral',
'confidence': 0.0
}
blob = TextBlob(text)
subjectivity = blob.sentiment.subjectivity
# implement Vader Analysis for polarity scores
if model == "Vader":
vader = SIA()
fullpolarity = vader.polarity_scores(text)
polarity=fullpolarity['compound']
polarity_thresh = 0.05
# otherwise
else:
polarity = blob.sentiment.polarity
polarity_thresh = 0.1
# Determine sentiment label through polarity threshold
if polarity > polarity_thresh:
label = 'positive'
elif polarity < -polarity_thresh:
label = 'negative'
else:
label = 'neutral'
# Calculate confidence (distance from neutral)
confidence = abs(polarity)
res = {
'polarity': polarity,
'subjectivity': subjectivity,
'label': label,
'confidence': confidence
}
return res
def process_news_articles(self, articles: List[Dict], model: str) -> pd.DataFrame:
"""
Process news articles and add sentiment analysis
Args:
articles: List of news articles from API
Returns:
DataFrame with processed articles and sentiment data
"""
processed_articles = []
for article in articles:
# Skip articles with missing essential data
if not article.get('title') or not article.get('publishedAt'):
continue
# Analyze sentiment of title and description
title_sentiment = self.analyze_sentiment(article['title'], model=model)
description_sentiment = self.analyze_sentiment(article['description'], model=model)
# Combine title and description sentiment (weighted toward title)
combined_polarity = (title_sentiment['polarity'] * 0.7 +
description_sentiment['polarity'] * 0.3)
combined_subjectivity = (title_sentiment['subjectivity'] * 0.7 +
description_sentiment['subjectivity'] * 0.3)
# Determine overall sentiment
if combined_polarity > 0.1:
overall_sentiment = 'positive'
elif combined_polarity < -0.1:
overall_sentiment = 'negative'
else:
overall_sentiment = 'neutral'
processed_article = {
'title': article['title'],
'description': article.get('description', ''),
'url': article['url'],
'source': article['source']['name'],
'published_at': article['publishedAt'],
'author': article.get('author', 'Unknown'),
'sentiment_label': overall_sentiment,
'sentiment_polarity': combined_polarity,
'sentiment_subjectivity': combined_subjectivity,
'title_sentiment': title_sentiment['label'],
'title_polarity': title_sentiment['polarity'],
'description_sentiment': description_sentiment['label'],
'description_polarity': description_sentiment['polarity']
}
processed_articles.append(processed_article)
# Convert to DataFrame
df = pd.DataFrame(processed_articles)
# Convert published_at to datetime
if not df.empty:
df['published_at'] = pd.to_datetime(df['published_at'])
df = df.sort_values('published_at', ascending=False)
return df
def get_ai_news_with_sentiment(self,
query: str = "artificial intelligence",
days: tuple[int] = (7,14),
sources: Optional[str] = None,
model: str = "Textblob") -> pd.DataFrame:
"""
Complete pipeline: fetch news and analyze sentiment
Args:
query: Search query for news articles
days: Number of days to look back
sources: Comma-separated string of news sources
Returns:
DataFrame with news articles and sentiment analysis
"""
print(f"Fetching {query} news from the last {days} days...")
# Fetch articles
articles = self.fetch_ai_news(query=query, days=days, sources=sources)
if not articles:
print("No articles found.")
return pd.DataFrame()
print(f"Found {len(articles)} articles. Analyzing sentiment...")
# Process and analyze
df = self.process_news_articles(articles, model=model)
print(f"Processed {len(df)} articles with sentiment analysis. \nUsed {model} for polarity analysis and Textblob for sentiment analysis.")
return df
def load_config():
"""Load configuration from config.json"""
with open('config.json', 'r') as f:
return json.load(f)
if __name__ == "__main__":
# Test the API when run directly
analyzer = AINewsAnalyzer()
config = load_config()
print("Testing AI News Sentiment Analyzer...")
print("=" * 50)
# Test sentiment analysis
test_texts = config["test_texts"]
print("\nSentiment Analysis Examples:")
for text in test_texts:
sentiment = analyzer.analyze_sentiment(text)
print(f"Text: {text}")
print(f"Sentiment: {sentiment['label']} (polarity: {sentiment['polarity']:.2f}\n")
# Test news fetching
print("Fetching recent AI news...")
df = analyzer.get_ai_news_with_sentiment(days=3)
if not df.empty:
print(f"\nFound {len(df)} articles")
print("\nSentiment Distribution:")
print(df['sentiment_label'].value_counts())
print("\nTop 3 Most Positive Headlines:")
positive_articles = df[df['sentiment_label'] == 'positive'].nlargest(3, 'sentiment_polarity')
for _, article in positive_articles.iterrows():
print(f"📈 {article['title']} (Score: {article['sentiment_polarity']:.2f})")
print("\nTop 3 Most Negative Headlines:")
negative_articles = df[df['sentiment_label'] == 'negative'].nsmallest(3, 'sentiment_polarity')
for _, article in negative_articles.iterrows():
print(f"📉 {article['title']} (Score: {article['sentiment_polarity']:.2f})")
else:
print("No articles found. Check your API key and internet connection.") |