How to extract Twitter data with Twint?

One of the more popular ways to analyze data from Twitter is to use the Twitter API, called Tweepy, which gives you different levels of access depending on what you want to use it for.

However, Tweepy has its limitations. First, you need to create a Twitter developer account and request access to the API. You need to answer a series of questions to do this, which is very time-consuming. Even if you are approved, there is a limit to the number of tweets you can crawl.

To solve this problem, here is a recommended alternative to Tweepy - Twint.

What is Twint?

Twint is an advanced Twitter scraping tool written in Python that allows for scraping Tweets from Twitter profiles without using Twitter's API.

While the Twitter API only allows you to scrape 3200 Tweets at once, Twint has no limit.

It is very quick to set up, and you don't need any kind of authentication or access permission.

How to extract?

First, install the Twint library:

Then, run the following lines of code to scrape Tweets related to a topic. In this case, I'm going to scrape every Tweet that mentions Taylor Swift:

Finally, all you need to is read the .csv file back into a data frame:

Taking a look at the head of the data-frame, we see output that looks like this:

The contents of all Tweets are stored in the 'tweet' column:

Running the above line of code will render the content of all Tweets:

Uses of tweet extraction:

There are many potential uses for tweet extraction.

1. Social media monitoring:

Companies and organizations can monitor social media for mentions of their brand or product, allowing them to respond quickly to customer feedback or complaints.

2. Market research:

By extracting tweets related to specific industries or topics, it can provide valuable insights into consumer trends and preferences.

3. Sentiment Analysis:

By extracting tweets and analyzing their content, sentiment analysis can be performed to determine the overall sentiment of a specific topic or brand.

4. News aggregation:

By extracting tweets related to breaking news stories, real-time updates and information can be provided.

5. Competitive Analysis:

By extracting tweets from competitors, companies can gain valuable insights into their strategies and products.

6. Influencer Identification:

Influential social media users can be identified based on the number of followers, engagement rates, and other metrics.

7. Content Creation:

By analyzing popular tweets and trending topics, content creators can gain inspiration to create their own social media posts and articles.