general updates made, updated lots of stuff
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klein panic
251
src/WebScraper/scrapers/oil_news_preprocessor.py
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251
src/WebScraper/scrapers/oil_news_preprocessor.py
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import json
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import re
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import os
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import time
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from selenium import webdriver
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from selenium.webdriver.firefox.options import Options
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from selenium.webdriver.common.by import By
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from selenium.webdriver.support.ui import WebDriverWait
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from selenium.webdriver.support import expected_conditions as EC
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from bs4 import BeautifulSoup
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from tqdm import tqdm # Progress bar
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OIL_NEWS_URL = "https://oilprice.com/Latest-Energy-News/World-News/"
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SCRAPER_DIR = os.path.dirname(os.path.dirname(__file__)) # One level up
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DATA_DIR = os.path.join(SCRAPER_DIR, "data")
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KEYWORD_FILE_PATH = os.path.join(SCRAPER_DIR, "assets", "oil_key_words.txt")
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if not os.path.exists(DATA_DIR):
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os.makedirs(DATA_DIR)
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def load_existing_data(file_path):
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if os.path.exists(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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return []
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def save_to_json(data, file_path):
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existing_data = load_existing_data(file_path)
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existing_links = {article['link'] for article in existing_data if 'link' in article}
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new_data = []
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for article in data:
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if 'link' not in article or article['link'] in existing_links:
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print(f"Skipping duplicate or missing link article: {article.get('headline', 'Unknown Headline')}")
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continue
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new_data.append(article)
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combined_data = existing_data + new_data
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with open(file_path, 'w', encoding='utf-8') as f:
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json.dump(combined_data, f, ensure_ascii=False, indent=4)
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print(f"Data saved to {file_path}")
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def load_keyword_importance(file_path):
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keyword_importance = {}
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if os.path.exists(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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parts = line.strip().split()
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if len(parts) == 2:
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keyword, importance = parts
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keyword_importance[keyword.lower()] = int(importance)
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else:
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print(f"Keyword file not found at {file_path}")
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return keyword_importance
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keyword_importance = load_keyword_importance(KEYWORD_FILE_PATH)
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def extract_keywords(text, keyword_importance):
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words = re.findall(r'\b\w+\b', text.lower())
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keywords = {word: keyword_importance[word] for word in words if word in keyword_importance}
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return sorted(keywords.items(), key=lambda x: x[1], reverse=True)[:10]
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def filter_content(content):
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"""Remove advertisements, irrelevant phrases, headers, and disclaimers from content."""
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patterns = [
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r'ADVERTISEMENT',
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r'Click Here for \d+\+ Global Oil Prices',
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r'Find us on:',
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r'Back to homepage',
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r'Join the discussion',
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r'More Top Reads From Oilprice.com',
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r'©OilPrice\.com.*?educational purposes',
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r'A Media Solutions.*?Oilprice.com',
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r'\"It\'s most important 8 minute read of my week…\"',
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r'^[\w\s]*?is a [\w\s]*? for Oilprice\.com.*?More Info',
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r'^.*?DNOW is a supplier.*?,',
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]
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for pattern in patterns:
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content = re.sub(pattern, '', content, flags=re.IGNORECASE)
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content = re.sub(r'\s+', ' ', content).strip()
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return content
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def scrape_author_info(driver, author_url, headline_pages=1):
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"""Scrape author's name, bio, contributor since date, and latest article headlines with excerpts, keywords, and timestamp."""
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author_name = "Unknown"
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author_bio = ""
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contributor_since = ""
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other_articles = []
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try:
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# Load author page
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driver.get(author_url)
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WebDriverWait(driver, 15).until(
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EC.presence_of_element_located((By.TAG_NAME, "h1"))
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)
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page_source = driver.page_source
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bio_soup = BeautifulSoup(page_source, "html.parser")
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# Extract author name
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author_name_tag = bio_soup.find('h1')
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author_name = author_name_tag.get_text(strip=True) if author_name_tag else "Unknown Author"
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# Extract author bio
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author_bio_tag = bio_soup.find('div', class_='biography')
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author_bio = author_bio_tag.get_text(strip=True) if author_bio_tag else "No bio available"
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# Extract contributor since date
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contributor_since_tag = bio_soup.find('p', class_='contributor_since')
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contributor_since = contributor_since_tag.get_text(strip=True).replace("Contributor since: ", "") if contributor_since_tag else "Unknown Date"
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# Extract latest articles by author with heading, excerpt, keywords, and timestamp
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for page in range(1, headline_pages + 1):
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driver.get(f"{author_url}/Page-{page}.html")
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WebDriverWait(driver, 10).until(
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EC.presence_of_element_located((By.CLASS_NAME, "articles"))
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)
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page_soup = BeautifulSoup(driver.page_source, "html.parser")
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article_tags = page_soup.find_all('li', class_='clear')
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for article in article_tags:
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heading_tag = article.find('h3')
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excerpt_tag = article.find('p', class_='articlecontent')
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timestamp_tag = article.find('div', class_='meta')
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if heading_tag and excerpt_tag and timestamp_tag:
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heading = heading_tag.get_text(strip=True)
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excerpt = filter_content(excerpt_tag.get_text(strip=True)) # Use filter_content
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timestamp = timestamp_tag.get_text(strip=True).split("|")[0].replace("Published ", "").strip()
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keywords = [keyword for keyword, _ in extract_keywords(excerpt, keyword_importance)]
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other_articles.append({
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"heading": heading,
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"excerpt": excerpt,
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"keywords": keywords,
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"published_date": timestamp
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})
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except Exception as e:
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print(f"Error scraping author info: {e}")
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author_name = "Error Occurred"
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author_bio = str(e)
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contributor_since = "N/A"
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other_articles = [{"heading": "Error retrieving articles", "excerpt": "", "keywords": [], "published_date": ""}]
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return {
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"name": author_name,
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"bio": author_bio,
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"contributor_since": contributor_since,
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"other_articles": other_articles
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}
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def scrape_oil_news():
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print("Scraping oil news articles for sentiment analysis...")
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options = Options()
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options.headless = True
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driver = webdriver.Firefox(options=options)
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news_data = []
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page_number = 1
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max_pages = 1
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total_articles = 0
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while page_number <= max_pages:
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driver.get(f"{OIL_NEWS_URL}Page-{page_number}.html")
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try:
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WebDriverWait(driver, 10).until(
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EC.presence_of_element_located((By.CLASS_NAME, "categoryArticle"))
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)
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except:
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break
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soup = BeautifulSoup(driver.page_source, "html.parser")
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total_articles += len(soup.find_all('div', class_='categoryArticle'))
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page_number += 1
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page_number = 1
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with tqdm(total=total_articles, desc="Scraping articles", unit="article") as pbar:
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while page_number <= max_pages:
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print(f"\nProcessing page {page_number}...")
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driver.get(f"{OIL_NEWS_URL}Page-{page_number}.html")
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soup = BeautifulSoup(driver.page_source, "html.parser")
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articles = soup.find_all('div', class_='categoryArticle')
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if not articles:
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break
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for article in articles:
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headline = article.find('h2', class_='categoryArticle__title').get_text(strip=True) if article.find('h2', class_='categoryArticle__title') else None
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link_tag = article.find('a', href=True)
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link = link_tag['href'] if link_tag else None
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date_meta = article.find('p', class_='categoryArticle__meta')
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date = date_meta.get_text(strip=True).split('|')[0].strip() if date_meta else None
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content = ""
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if link:
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print(f"Fetching article: {link}")
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driver.get(link)
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try:
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WebDriverWait(driver, 10).until(
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EC.presence_of_element_located((By.CLASS_NAME, "singleArticle"))
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)
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article_soup = BeautifulSoup(driver.page_source, "html.parser")
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raw_content = " ".join([p.get_text(strip=True) for p in article_soup.find_all('p')])
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content = filter_content(raw_content)
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# Fetch author info using scrape_author_info
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author_url = article_soup.find('a', text=re.compile(r'More Info|Read More', re.IGNORECASE))['href']
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author_info = scrape_author_info(driver, author_url, headline_pages=1)
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except:
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print(f"Error: Content did not load for article {headline}.")
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author_info = {
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"name": "Unknown",
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"bio": "",
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"contributor_since": "",
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"other_articles": []
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}
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extracted_keywords = extract_keywords(f"{headline} {content}", keyword_importance)
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if headline and link and date:
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news_data.append({
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'headline': headline,
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'link': link,
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'content': content,
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'date': date,
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'author': author_info['name'],
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'author_bio': author_info['bio'],
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'contributor_since': author_info['contributor_since'],
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'other_articles': author_info['other_articles'],
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'keywords': extracted_keywords,
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})
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pbar.set_postfix_str(f"Processing article: {headline[:40]}...")
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pbar.update(1)
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page_number += 1
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time.sleep(2)
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driver.quit()
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return news_data
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def run_preprocessor():
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file_path = os.path.join(DATA_DIR, 'preprocessed_oil_news.json')
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news_data = scrape_oil_news()
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save_to_json(news_data, file_path)
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if __name__ == "__main__":
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run_preprocessor()
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