Sentiment Analysis

Four sentiment analysis techniques were applied to the data: VADER and NLTK, lexicon-based models used for polarity determination. Given its robustness and advantages, Flair was chosen as the primary model for sentiment analysis in this study. Of the total tweets, 453 were unanimously classified as positive by all models, reflecting hope around vaccination and safety measures. 

Lexical analysis using TextBlob provided insights into the most salient terms driving sentiment. Positive words included “vaccine,” “hope,” “joy”, and “safety,” while negative terms such as “death”, “terrible”, “masks” and “fear” dominated the discourse on pandemic restrictions.