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from transformers import pipeline
from rapidfuzz.fuzz import partial_ratio
import yfinance as yf
import pandas as pd
def analyze_headline_sentiment(headlines):
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
results = []
for headline in headlines:
sentiment = sentiment_pipeline(headline)[0]
results.append({
"headline": headline,
"sentiment": sentiment['label'],
"score": sentiment['score']
})
return pd.DataFrame(results)
def filter_news_by_sentiment(ticker_symbol, news_items, fuzzy_threshold=60):
ticker = yf.Ticker(ticker_symbol)
company_name = ticker.info.get('longName', ticker_symbol)
def extract_text(item):
content = item.get('content', '')
if isinstance(content, dict):
return content.get('title', str(content))
elif isinstance(content, str):
return content
else:
return str(content)
filtered_news = []
for item in news_items:
text = extract_text(item)
text_lower = text.lower()
ticker_present = ticker_symbol.lower() in text_lower
similarity = partial_ratio(company_name.lower(), text_lower)
if ticker_present or similarity >= fuzzy_threshold:
filtered_news.append(text)
if len(filtered_news) < 5:
for item in news_items:
text = extract_text(item)
if text not in filtered_news:
filtered_news.append(text)
if len(filtered_news) >= 5:
break
return filtered_news[:7] # Return top 7 headlines |