Get UNLIMITED Tweets by Python Without Twitter API
TLDRIn this video, the creator demonstrates a method to access an unlimited number of tweets without using the Twitter API or authentication. By employing the Python package 'sn' for social network scraping and 'pandas' for data analysis, the video showcases how to collect and analyze tweet data based on specific queries, user accounts, and time frames. The process is exemplified by gathering 5,000 tweets from Elon Musk between 2010 and 2020, highlighting the potential for complex data mining and analysis beyond the limitations of the official API.
Takeaways
- 🚀 Bypass Twitter API limitations by using a Python package called 'sn' for scraping tweets without authentication.
- 💇 The video creator's previous video on the Twitter API prompted many comments highlighting its restrictions, such as a 3200 tweet limit and a 7-day age limit.
- 🛠️ Install necessary packages for the task by running 'pip install sn' for social network scraping and 'pip install pandas' for data manipulation.
- 📝 Start by creating a new Python file and importing the required 'sn' and 'pandas' modules.
- 🔍 Construct a query string to begin with and use 'sn.twitter.twitter' to search for tweets related to the query.
- 📊 Use 'pandas' to display the tweet data in a structured format, such as a DataFrame.
- 📈 Initially, limit the number of tweets to a manageable amount (e.g., 100) for demonstration purposes.
- 🔄 Append tweets to a list based on the query and break the loop when the limit is reached.
- 🔍 Utilize Twitter's advanced search features to refine the query and target specific users, dates, and keywords.
- 🎯 Aim for a complex query to retrieve a large number of tweets (e.g., 5000 tweets from Elon Musk between 2010 and 2020).
- 📊 After gathering the tweets, perform further analysis such as sentiment analysis using state-of-the-art models from the Facebook AI team.
Q & A
What is the main topic of the video?
-The main topic of the video is how to get unlimited tweets without using the Twitter API or any authentication.
What limitations of the Twitter API does the video address?
-The video addresses the limitations of the Twitter API such as the inability to get more than 3200 user tweets and the restriction to tweets that are only seven days old.
Which Python package is used for scraping tweets in the video?
-The Python package used for scraping tweets in the video is called 'sn' (social network scraping).
What additional package is installed for data handling in the video?
-The additional package installed for data handling in the video is 'pandas'.
How does the video demonstrate the initial setup for tweet scraping?
-The video demonstrates the initial setup by installing the required packages, importing the necessary modules, and writing a basic query to begin scraping tweets.
What information is extracted from each tweet in the video?
-The information extracted from each tweet includes the URL, date, content, user information such as username and user ID.
How does the video handle a large number of tweets?
-The video handles a large number of tweets by creating a data frame using the pandas library to organize and display the tweets' information.
What is the purpose of changing the query in the script?
-The purpose of changing the query is to make the search more complex, allowing the user to specify accounts, dates, and other criteria for the tweets they want to retrieve.
How does the video show an example of a complex search?
-The video shows an example of a complex search by using Twitter's advanced search on the website to find 5000 tweets from Elon Musk between 2010 and 2020, and then applying those search parameters to the Python script.
What can be done with the collected tweets according to the video?
-According to the video, one can perform sentiment analysis on the collected tweets using a state-of-the-art model from the Facebook AI team.
How long does it take to retrieve a large number of tweets as demonstrated in the video?
-It takes approximately two minutes to retrieve 5000 tweets in the example provided in the video.
Outlines
🚀 Getting Started with Unrestricted Tweet Scraping
The video begins by introducing a method to scrape tweets without the limitations of the Twitter API or the need for authentication. The speaker shares their excitement about overcoming the API's restrictions, such as the 3200-tweet limit and the seven-day age limit. They proceed to demonstrate the installation of necessary Python packages, including 'sn.scrape' for social network scraping and 'pandas' for data manipulation. The speaker then creates a Python script to import the required modules and begins constructing a query to fetch tweets. The initial query is simple, but the speaker explains that it will be expanded upon later in the video. The process of fetching and printing tweets is shown, along with examining the attributes of a tweet, such as its URL, date, content, and user information. The speaker concludes the paragraph by mentioning the creation of a data frame to organize and display tweet information effectively.
🔍 Advanced Tweet Scraping: Custom Queries and Sentiment Analysis
In the second paragraph, the speaker delves into refining the tweet scraping process by altering the query to target specific tweets. They guide the viewer through using Twitter's advanced search feature to set precise criteria, such as a specific user's tweets and a custom date range. The speaker exemplifies this by searching for Elon Musk's tweets from 2010 to 2020 and copying the search parameters into the Python code. The limit is adjusted to 5000 tweets to accommodate the broader search, and the speaker demonstrates the code's execution, which takes a couple of minutes to fetch the desired tweets. The paragraph concludes with a mention of potential applications for the scraped tweets, such as performing sentiment analysis using a model from the Facebook AI team, and encourages viewers to check out a related video for more information on this topic. The speaker also invites viewers to like and subscribe for more helpful content.
Mindmap
Keywords
💡Twitter API
💡Authentication
💡Python
💡SNScrape
💡Pandas
💡Tweet
💡Data Frame
💡Sentiment Analysis
💡Advanced Search
💡Code
Highlights
The video demonstrates a method to obtain unlimited tweets without using the Twitter API or authentication.
The limitations of the Twitter API, such as the 3200 tweet limit per user and tweets only up to 7 days old, are addressed in this video.
The process uses a Python package called 'sn' for social network scraping.
The video provides a step-by-step guide on installing the required packages 'sn' and 'pandas'.
A new Python file 'twitch.pi' is created to write the code.
The initial code imports necessary modules and sets up a basic query structure.
The script uses 'sn.twitter' to search for tweets and 'pandas' to display the data.
An example query is set up to search for tweets related to the word 'python'.
The script prints out the structure of a tweet to understand the available attributes.
Tweets are collected into a list and limited to a set number, for example, 100 tweets.
The collected tweets are converted into a pandas DataFrame for easier data analysis.
The video shows how to modify the query for more complex searches, such as tweets from a specific user within a date range.
An example is given where 5000 tweets from Elon Musk between 2010 and 2020 are collected.
The video concludes by suggesting further analysis of the collected tweets, such as sentiment analysis.
The video provides a practical workaround for the limitations of the Twitter API for users needing large volumes of tweet data.
The method allows for the collection of tweets without the need for authentication, making it accessible for various use cases.
The video is a tutorial on using the 'sn' package for scraping social media data, specifically Twitter.
The script is designed to be simple and easy to follow, making it suitable for beginners in data scraping.
Transcripts
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