Twitter Sentiment Analysis Using Python

The video begins with a welcome to a tutorial on Python programming and machine learning, specifically focusing on Twitter sentiment analysis. The presenter is using Google's Collab Research (Google Colab) for ease of programming in Python without installation. The first steps involve creating a new Python 3 notebook and writing a program description in a comment. The presenter then proceeds to import necessary libraries such as tweepy, textblob, wordcloud, pandas, numpy, and regular expressions, and sets a plot style for visualizations.

The paragraph details the process of setting up a Twitter application to authenticate and fetch tweets. The presenter explains the need for a Twitter account and application, and mentions a link in the description for guidance. The keys for the Twitter application are loaded from a CSV file using Google Colab's file upload functionality. The consumer key, consumer secret, access token, and access token secret are extracted and used to authenticate with the Twitter API.

The focus shifts to extracting tweets from Bill Gates' Twitter account, chosen for its positive impact. The presenter uses the tweepy library to fetch 100 tweets in English with the 'extended' tweet mode. The tweets are then printed, and a plan to clean the text data by removing URLs, hashtags, and other unwanted elements is introduced. A function named 'clean_text' is mentioned as a solution for text preparation.

The cleaning process is elaborated with the creation of a 'clean_text' function using regular expressions to remove unwanted characters, hashtags, retweets, and URLs from the tweets. The cleaned tweets are then stored in a pandas DataFrame with a 'tweets' column. The presenter demonstrates how to show the first few rows of the DataFrame and discusses further cleaning improvements.

The presenter introduces the concept of subjectivity and polarity in sentiment analysis, creating functions 'get_subjectivity' and 'get_polarity' to analyze the sentiment of tweets. These are added as new columns to the DataFrame. The sentiment distribution is visualized using a word cloud to show the frequency of words in the tweets. The presenter explains the meaning of the word cloud and its significance in understanding the sentiment of the text.

The video continues with the analysis of sentiment distribution by creating a new DataFrame column 'analysis' based on the polarity scores. This column categorizes tweets as positive, neutral, or negative. The presenter then sorts the tweets by polarity to print the most positive and negative tweets, providing insights into the sentiment of Bill Gates' recent tweets. The analysis reveals that 81% of the tweets are positive, 9% are negative, and 10% are neutral.

A scatter plot is created to visualize the polarity and subjectivity of the tweets. The x-axis represents polarity, and the y-axis represents subjectivity, with each point corresponding to a tweet. The majority of the points lie on the positive side of the neutral line, indicating a predominantly positive sentiment. The video also includes a count of positive, neutral, and negative tweets, confirming the earlier analysis.

The presenter concludes the sentiment analysis by plotting a bar chart to visualize the counts of positive, neutral, and negative tweets. The chart confirms that the majority of Bill Gates' tweets are positive, with a small number of negative tweets. The video ends with a summary of the process and an invitation for viewers to ask questions in the comments. The presenter encourages viewers to like and share the video if they found it helpful.