• VADER Lexicon
• TextBlob
• Machine Learning Models
• Word Embeddings

• Source: Crowflower Data for Everyone
• Twitter data of February 2015 was scraped, the tweets are classified into positive, negative & neutral emotions

63% of the tweets are negative, whereas 21% positive & 16% neutral

United Airlines followed by U.S Airlines have the highest tweet mentions

New York location has the highest tweet mentions

Eastern time followed by Central time are the timezones of most users

Accuracy of VADER Lexicon is 49.61%

Accuracy of TextBlob is 42.91%

The following models have the best performance

• Sentiment Analysis with Logistic Regression gives 78% accuracy & 0.77 AUC-ROC Value
• Word Embeddings with Gradient or XG Boost are good classifiers with 77% accuracy & 0.73 AUC-ROC Score