This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:07:14
In this video, the creator explains how to scrape Twitter data using the Python package 'snscrape' without needing authentication. He walks through the initial setup, which includes installing 'snscrape' and 'pandas,' and demonstrates how to write a script to extract and display tweet data such as URL, date, and content. The instructor focuses on creating a data frame to manage tweet data effectively, highlighting the importance of attributes like date, user, and content. For more advanced data collection, the video showcases the use of Twitter's advanced search features to filter tweets by specific users or dates, exemplified by retrieving 5,000 tweets from Elon Musk between 2010 and 2020. The speaker emphasizes the potential applications of the collected data, including sentiment analysis, and suggests further resources for using advanced models from Facebook AI for deeper analysis.
00:00:00
In this part of the video, the creator demonstrates how to obtain unlimited tweets without authentication using just a few lines of code. He introduces the Python package called ‘snscrape’ for social network scraping and installs it along with ‘pandas’ to display the data. He starts by creating a new Python file and writing the initial code to import the necessary modules. A basic query string ‘python’ is created to understand the tweet structure. He then sets up a for loop to iterate through tweets using the Twitter search scraper from ‘snscrape’, prints each tweet’s attributes using the `vars` function, and displays the tweet’s data including URL, date, and content.
00:03:00
In this part of the video, the speaker demonstrates how to handle and manipulate tweet data by creating a data frame using Python’s pandas module. Firstly, they decide to collect specific tweet attributes such as date, user, and content, and set a limit of 100 tweets. By looping through the tweets and checking if the length meets the limit, they append relevant tweet details to a list. After accumulating the tweets, they utilize pandas to create a data frame, specifying column names as date, user, and tweets. The tweets are then printed, showing a data frame with 100 tweets.
For more complex searches, the speaker suggests modifying the query. They navigate to Twitter’s website and explain the advanced search feature, which allows for filtering tweets by specific users and dates. To illustrate, they plan to fetch 5,000 tweets from Elon Musk between 2010 and 2020, using the advanced search to refine the query parameters.
00:06:00
In this part of the video, the speaker demonstrates how to retrieve a large dataset of tweets without using the Twitter API. They set a search query, copy the generated text into their Python code, adjust the tweet limit to 5000, and run the script. The process takes about two minutes to complete, yielding 5000 tweets from Elon Musk. The speaker then suggests using these tweets for sentiment analysis and mentions an additional video tutorial on how to use a model from Facebook AI for this purpose.