Exploring Sentiment on Campus. A Twitter Sentiment Analysis on University Tweets

Abstract

We report in this work the results of our analysis of accuracy of 5 sentiment analysis methods (TextBlob, VADER, logistic regression, support vector machine, CNN on encodings based on BERT tokenization,) for a dataset consisting of tweets from the academia domain, that we API-scraped for 32 universities during the year 2022. We show some results for the volume and sentiment polarity trends exhibited by this dataset. We connect peak and low sentiment averages to concrete events that explain the respective sentiment trend; this proves that observing the social media trends allows to detect real events that need attention and possible action.