Watch the lecture on the YouTube channel of the master.

Speaker: Prof. Serena Pelosi - UNiversity of Salerno

Abstract: the talk illustrates an innovative methodology for content-based Instagram posts analysis. A case study, in which linguistics, semiotics and design work together to analyse the population reactions to the Covid-19 pandemic. The research proposes a sentiment analysis based on Instagram users’ posts published during the lockdown period in Italy. The corpus is constituted by all posts containing the hashtag #andràtuttobene, published on Instagram on May 4, May 18 and June 3 2020. Our research gives a view on the pandemy on a national, regional and provincial scale. Thanks to a multidisciplinary approach, we analyzed different languages and expressions constituting the posts and provided a set of procedures revealing different polarity trends for each type of expression used. The aim is to analyse the pandemy through a selection of linguistic relevance and to propose a single comprehensive measure. The quantitative and qualitative approach required computational linguistics skills together with visualization techniques.

Short bio: Serena Pelosi is a researcher in text analysis and NLP at the University of Salerno. She is PhD in Computational Linguistics. She worked with Prof. Ruslan Mitkov, Director of the Research Group in Computational Linguistics at the University of Wolverhampton, UK. Currently research on automatic text analysis at the University of Salerno, led by Prof. Annibale Elia and Mario Monteleone. She is the author of many publications and participates in various projects both nationally and internationally.

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