sentiment_score.ipython

import pandas as pd 

#Extract text from articles 

article_text = ''. join(map(lambda p: p.text, soup.find_all('p')))
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import nltk
nltk.download('vader_lexicon')
import requests
from bs4 import BeautifulSoup
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Define the URL of the article on Yahoo.com
url = "https://www.yahoo.com/news/hezbollah-hesitates-israel-strikes-gaza-171550689.html"

# Send a GET request to the URL
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Parse the page content using BeautifulSoup
    soup = BeautifulSoup(response.content, "html.parser")

    # Find the article content based on the specific HTML tags
    article_content = soup.find('div', class_='caas-body').find_all('p')

    # Extract the text from the article content
    extracted_text = ""
    for paragraph in article_content:
        extracted_text += paragraph.get_text() + " "

    # Initialize the VADER sentiment analyzer
    sia = SentimentIntensityAnalyzer()

    # Analyze sentiment of the extracted text
    sentiment_scores = sia.polarity_scores(extracted_text)

    # Print the sentiment scores
    for sentiment, score in sentiment_scores.items():
        print(f"{sentiment}: {score}")

else:
    print(f"Failed to retrieve the article. Status code: {response.status_code}")


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import requests
from bs4 import BeautifulSoup

# Define the URL of the article on Yahoo.com
url = "https://www.yahoo.com/news/hezbollah-hesitates-israel-strikes-gaza-171550689.html"

# Send a GET request to the URL
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Parse the page content using BeautifulSoup
    soup = BeautifulSoup(response.content, "html.parser")

    # Find the article content based on the specific HTML tags
    article_content = soup.find('div', class_='caas-body').find_all('p')

    # Extract the text from the article content
    extracted_text = ""
    for paragraph in article_content:
        extracted_text += paragraph.get_text() + " "

    # Print the extracted text
    print(extracted_text)

else:
    print(f"Failed to retrieve the article. Status code: {response.status_code}")