Bitcoin Sentiment Analysis Using Python & Twitter

The video begins with a welcome and an introduction to the topic of analyzing Twitter sentiment on Bitcoin using Python. The creator intends to measure the positivity, neutrality, or negativity of Twitter users' opinions on Bitcoin. The video encourages viewers to subscribe and engage with the content. The creator uses Google's Collab platform for easy Python programming and explains the process of setting up the environment, including importing necessary libraries such as Tweepy, TextBlob, Pandas, and others.

The second paragraph details the process of setting up the Twitter API to gather tweets. The creator explains the need for a Twitter account and application to obtain API credentials, which are uploaded from a file. The video demonstrates how to use Google's library to upload the login file and extract the credentials for use in the program. The Twitter authentication object is created, and the API object is set up with rate limit considerations.

In this part, the creator focuses on gathering 2000 tweets about Bitcoin, specifically targeting tweets since November 1st, 2018, and filtering out retweets. The video explains the creation of a search term variable and the use of Tweepy's cursor object to fetch tweets. The tweets are stored in a variable and the process is executed, with the video pausing to allow the fetching to complete.

The creator moves on to cleaning the gathered tweets by removing hashtags, backslashes, and hyperlinks. A function named 'clean_twt' is defined to perform these cleaning tasks using regular expressions. The cleaned tweets are then stored in a new DataFrame column called 'cleaned_tweets'. The video demonstrates the application of the cleaning function and shows the original and cleaned tweets side by side for comparison.

The video continues with the analysis of sentiment by calculating subjectivity and polarity for each tweet. Functions 'get_subjectivity' and 'get_polarity' are created to extract these sentiment metrics using TextBlob. New columns are added to the DataFrame for subjectivity and polarity, and the video shows how to apply these functions to the tweets and display the updated data.

The creator introduces a function 'get_sentiment' to categorize tweets as positive, neutral, or negative based on the sentiment scores. A new column 'sentiment' is added to the DataFrame to store these sentiment categories. The video demonstrates the application of the sentiment categorization function and displays the resulting data, showing the distribution of sentiment across the tweets.

The video concludes with visualizing the sentiment analysis results using a scatter plot for subjectivity and polarity, and a bar chart for the count of positive, neutral, and negative sentiments. The creator interprets the scatter plot, noting the majority of tweets are neutral or positive towards Bitcoin. The bar chart confirms this, with the majority of tweets being neutral and a smaller number being positive or negative. The video ends with a call to action for Patreon supporters and a link to resources in the video description.