Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy.

Résumé

The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word "vaccin". A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.