Background:
An article by New York Times wrote: "The U.S. is falling to the lowest vaccination rates of the world's wealthiest democracies."

• Project Overview
• Code and Resources Used
• Database Schema
• Data Cleaning
• Analysis Results
• Limitations
• Contributors

Problem statement:

1. US is experiencing significant decrease in COVID-19 vaccination rate.
2. Evaluate President Joe Biden's sentiments towards COVID-19 vaccinations on Twitter and its impact on US vaccination rate.

Python Version: 3.9
Packages: Tweepy, Pandas, Time, RE, Sqlalchemy, Matplotlib, NLTK, Wordcloud, Numpy, Pillow, Itertools, Collections, Textblob, Datetime, Decimal
Dataset: Utilized Our World In Data's COVID Deaths dataset to observe Biden's tweets impact

The data crawled will be loaded into 3 tables and stored in a PostGreSQL database namely:

user_info:

• Twitter user id
• User's name
• User's profile description
• User's location

tweets:

• Tweet ID
• Tweet
• Tweet's like counts
• Tweet's retweet counts
• User's tweet
• User's name

user_social_network:

• User's followers count
• User's follow count
• User's name

• Crawled tweets are filtered based on keywords "vaccinated" and "covid-19"
• Tweets are stored in a list
• Tweets are changed to lowercase and split into individual elements
• Unwanted characters and URLs are removed using Regular Expressions library
• Stop words are removed using NLTK library
• Words are then sent for analysis

• After pre-processing Joe Biden’s tweets by removing stop words and url links, word cloud (on the right) was created using python in Jupyter notebook.
• The size of each word indicates its frequency in his tweets. The bigger the word, the higher the frequency count. Other than the two search keywords “vaccinated” and “covid-19”, the most frequently appearing words are “get”, and “vaccine”.

• Using the python module “Textblob”, we calculated the polarity score of his tweets with a range of -1 to 1. Values closer to 1 indicate more positivity, while values closer to -1 indicate more negativity.
• We then categorized all polarity scores into 3 buckets with a positive tweet having a polarity score > 0, neutral tweet having a score of 0 and negative tweet having a score of < 0
• From the pie chart on the left, we can observe that Joe Biden’s tweets are largely positive at 58%, hence, we can conclude his sentiments about vaccination for covid-19 are more positive.

Red lines indicate when the President tweeted about vaccination

• From the previous observations we know that Joe Biden's sentiment towards getting vaccinated is positive and he also has a high level of influence on Twitter as compared to Boris Johnson on the same issue. 
• The assumption then is that Joe Biden's tweets should have a positive and powerful impact on Covid-19's vaccination rate in USA.
• However, as shown in the below graph of Joe Biden's tweet date vs number of new vaccinations, there is no obvious trend to support our assumption. It is therefore inconclusive. 

• Limited number of tweets pulled due to twitter constraints (7 days limitation)
• Popularity score and reach score might not be a sufficient influence indicator of JoeBiden. Better indicator would be klout whereby it takes all his social media presence into account
• To better measure the impact of Biden's tweet on vaccination rate, we would need to execute specific statistical analysis such as time series which will take longer than 2 weeks to complete.
• Textblob may not be sophisticated enough to properly determine if a statement is truly "positive" or "negative"​
• Accuracy of analysis may not be good enough as usually large amount of data is needed

Hello there! We are a group of mid-career switchers undergoing a 3 months bootcamp in Business Intelligence and Data Analytics. This is our 2 weeks interim project where we showcased what we have learnt and brainstormed a solution to a given problem.

By Team Yellow Minions

• Alaric Tan
• Chloe Li
• Gao Shuai
• Olivia Chan
• Sin Lih Chin