Since the beginning of the vaccination campaign against Covid-19 in our country, resistance to vaccination has emerged on the part of a not negligible portion of the Italian population. Emotions (such as sadness, fear, etc.) and the polarity (positive / negative) of an opinion published on social media are essential for analyzing people’s position towards a topic. For this reason, we applied two Natural Language Processing tools, FEEL-IT and SentIta, to a few thousands of social networks posts against the COVID-19 vaccine or specifically the booster shot. We find out some significant insights about the prevalent emotions among users and propose to combine the outputs of the tools in order to increase the classification performance of an opinion according to three possible sentiments (positive/neutral/negative).
Comparing Emotion and Sentiment Analysis Tools on Italian anti-vaccination for COVID-19 posts
Elena Bellodi;Alessandro Bertagnon;Marco Gavanelli
2022
Abstract
Since the beginning of the vaccination campaign against Covid-19 in our country, resistance to vaccination has emerged on the part of a not negligible portion of the Italian population. Emotions (such as sadness, fear, etc.) and the polarity (positive / negative) of an opinion published on social media are essential for analyzing people’s position towards a topic. For this reason, we applied two Natural Language Processing tools, FEEL-IT and SentIta, to a few thousands of social networks posts against the COVID-19 vaccine or specifically the booster shot. We find out some significant insights about the prevalent emotions among users and propose to combine the outputs of the tools in order to increase the classification performance of an opinion according to three possible sentiments (positive/neutral/negative).I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
