| |
|
| | from bs4 import BeautifulSoup
|
| | import requests
|
| | from transformers import pipeline
|
| | from gtts import gTTS
|
| |
|
| |
|
| | def scrape_news(company_name):
|
| | url = f"https://news.google.com/search?q={company_name}"
|
| | response = requests.get(url)
|
| | soup = BeautifulSoup(response.text, 'html.parser')
|
| | articles = []
|
| | for item in soup.find_all('article')[:10]:
|
| | title = item.find('a', class_='DY5T1d').text
|
| | link = "https://news.google.com" + item.find('a', class_='DY5T1d')['href']
|
| | summary = item.find('div', class_='xBbh9').text if item.find('div', class_='xBbh9') else ""
|
| | articles.append({"title": title, "link": link, "summary": summary})
|
| | return articles
|
| |
|
| |
|
| | def analyze_sentiment(text):
|
| | sentiment_pipeline = pipeline("sentiment-analysis")
|
| | result = sentiment_pipeline(text)[0]
|
| | return result['label']
|
| |
|
| |
|
| | def comparative_analysis(articles):
|
| | sentiment_counts = {"Positive": 0, "Negative": 0, "Neutral": 0}
|
| | for article in articles:
|
| | sentiment_counts[article["sentiment"]] += 1
|
| | return sentiment_counts
|
| |
|
| | def generate_hindi_tts(text, filename="output.mp3"):
|
| | tts = gTTS(text, lang='hi')
|
| | tts.save(filename)
|
| | return filename
|
| |
|
| |
|
