Twitter Scraper: Automated Tweet Collection & Market Trend Insights

import requests
from bs4 import BeautifulSoup

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
    "Accept-Language": "en-US,en;q=0.8"
}

def fetch_twitter_post(url):
    session = requests.Session()
    response = session.get(url, headers=HEADERS)

    if response.status_code != 200:
        return {"error": "Unable to retrieve data"}

    soup = BeautifulSoup(response.text, "html.parser")

    tweet_text = soup.find("div", {"data-testid": "tweetText"})
    username = soup.find("div", {"data-testid": "User-Name"})
    likes = soup.find("div", {"data-testid": "like"})
    reposts = soup.find("div", {"data-testid": "retweet"})
    timestamp = soup.find("time")

    return {
        "Username": username.text.strip() if username else "Not Available",
        "Tweet_Content": tweet_text.text.strip() if tweet_text else "Not Available",
        "Likes_Count": likes.text.strip() if likes else "0",
        "Reposts_Count": reposts.text.strip() if reposts else "0",
        "Posted_On": timestamp["datetime"] if timestamp else "Unknown"
    }

# Example Twitter post URL
twitter_post_url = "https://twitter.com/example/status/1234567890"

tweet_data = fetch_twitter_post(twitter_post_url)
print(tweet_data)