Building a News Sentiment Analyzer with Web Scraping

Building a News Sentiment Analyzer with Web Scraping Sentiment analysis on news articles can predict market movements, track brand reputation, and identify trending narratives. Here is how to build one from scratch with Python. What We Are Building News scraper that collects articles from multiple sources Sentiment engine using TextBlob and VADER Trend tracker that monitors sentiment over time Dashboard to visualize results Setup pip install requests beautifulsoup4 textblob vaderSentiment pandas matplotlib python - m textblob . download_corpora The News Scraper import requests from bs4 import BeautifulSoup from datetime import datetime import time class NewsScraper : def __init__ ( self , proxy_key = None ): self . session = requests . Session () self . proxy_key = proxy_key self . session . headers . update ({ " User-Agent " : " Mozilla/5.0 (Windows NT 10.0; Win64; x64) " }) def fetch ( self , url ): if self . proxy_key : api_url = f " http://api.scraperapi.com?api_key= { self . proxy