Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects

Abstract

The accelerated development of Covid-19 vaccines offered tremendous promise and hope, yet stirred significant trepidation and fear. These conflicting emotions motivated many to turn to social media to share their experiences and side effects during the process of getting vaccinated. This paper analyzes sentiment and emotions from tweets collected using the hashtag #sideffects during the early roll out of the Covid-19 vaccine. Each tweet was labeled according to its sentiment polarity (positive vs. negative), and was assigned one of four emotion labels (joy, gratitude, apprehension, and sadness). Exploratory analysis of the tweets through word cloud visualizations revealed that the negativity of emotions intensified with the severity of side effects. Word and numerical features extracted from the text of the tweets and metadata were used to train conventional machine learning and deep learning models. These models resulted in an accuracy of 81% for binary sentiment classification, and 71 % for multi-label emotion identification. The proposed framework, which yielded competitive performance, may be employed to gain insights into people's thoughts and feelings from vaccine-related conversations. These insights can be helpful in devising communication and education strategies to mitigate vaccine hesitancy.