Scrape Most Reviewed News and Tweet Using Python

It’s important to stay up-to-date and deliver information quickly. Automating the news gathering process Filtering by reviews And sharing on platforms like Twitter can save time and provide value to your followers. In this project, we will use Python to scrape the most verified news articles and manually tweet them. The project focuses on cryptocurrency news trends which was copied from CoinTelegraph.com and filter by number of reviews.

Tools and Technologies Used

To accomplish this task, we use various Python modules that allow us to interact with Twitter’s API, collect data, and shorten URLs. Below are the main libraries required:

1. Tweepy: This is the official Twitter API Python client. This allows us to programmatically tweet news articles.
   • Install via pip:

pip install tweepy

2. PyMongo: MongoClient enables connections to a MongoDB database where we store and retrieve the news articles.

• Install via pip:

pip install pymongo

3. Pyshorteners: This module helps to shorten URLs, which is especially useful when posting to platforms with character limits, such as Twitter.

• Install via pip:

pip install pyshorteners

Step-by-Step Project Implementation

1. Twitter API Authentication

Before accessing Twitter, we need to authenticate using the Twitter API. Follow the steps below to get your API key token:

• Go to the Twitter developer portal and create a new app.
• Copy the consumer ID Confidential consumer information Access token and the access token secret from the app dashboard.

These credentials allow Tweepy to access your Twitter account to post.

2. Scraping and Storing News Articles

The first thing to do is scrape the most verified news articles from CoinTelegraph.com Using Python’s built-in HTMLParser and MongoDB, we extract the URL, name, and number of reviews for each news article.

class MyHTMLParser(HTMLParser):
    def handle_starttag(self, tag, attrs):
        global NewsArrayIndex
        if tag == "a":
            for name, value in attrs:
                if name == "href":
                    NewsArray[NewsArrayIndex] = value
                    NewsArrayIndex += 1

Information Including title information The review count and URL are stored in a MongoDB archive for further processing.

3. Selecting the Most Reviewed Articles

Once collected Articles are sorted by number of reviews. This allows us to tweet the top three trending articles of the day.

sortedNews = sorted(News, key=lambda x: int(x[1]), reverse=True)

4. Shortening URLs

To ensure the tweets stay within Twitter’s character limit (280 characters), we shorten the URLs using Pyshorteners and the Bitly API.

b = Shortener(api_key=BITLY_ACCESS_TOKEN)
response1 = b.bitly.short(NewsArray[0])  # Shortens the first news link

5. Tweeting the Top 3 Articles

The project will generate daily tweets when the most reviewed news articles are ready and their URLs are shortened. Each tweet will have a short headline. and links to news articles Add relevant hashtags to increase the visibility of your tweets.

dailyNews += f"{news1FewWords[0]} {news1FewWords[1]} {news1FewWords[2]}...{response1}\n"
dailyNews += f"#bitcoin #cryptocurrency #blockchain"
status = api.update_status(status=dailyNews)

6. Storing Tweeted Articles

To follow news tweets Published articles are stored in a MongoDB archive, which allows articles to be easily retrieved for future reference or reposting.

db1.Collection_tweeted_news.insert(datas)

Conclusion

This project automates the process of finding, curating, and tweeting popular cryptocurrency news based on the number of reviews. You can create a fully automated news sharing system using Python libraries such as Tweepy, PyMongo, and Pyshortener. This is a great project for those who want to work with APIs, databases, and web scraping.

Benefits:

• Share news automatically , reduce manual effort
• Engage your audience with trending, high-quality content.
• MongoDB is used for data storage and auditing purposes.

Follow the steps in this project. You can set up a similar system to tweet trending news. It’s not just articles related to cryptocurrencies. This approach can be extended to any industry or source. To curate personalized content for your audience