#andràtuttobene: Images, Texts, Emojis and Geodata in a Sentiment Analysis Pipeline
Authors: Pierluigi Vitale, Serena Pelosi, Mariacristina Falco (Università di Salerno, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This research investigates Instagram users’ sentiment narrated during the lockdown period in Italy, caused by the COVID-19 pandemic. The study is based on the analysis of all the posts published on Instagram under the hashtag #andràtuttobene on May 4, May 18 and June 3, 2020. Our research carried out a view on a national, regional and provincial scale. We analyzed all the different languages and forms (i.e. captions, hashtags, emojis and images) that constitute the posts. The aim of this research is to provide a set of procedures revealing the different polarity trends for each kind of expression and to propose a single comprehensive measure.

Introduction
This paper investigates the case of the Italian most used hashtag about the lockdown period for the COVID-19 pandemic on Instagram: #andràtuttobene1. The research team collected 7,482 posts, the entire amount published in three specific dates: May 4, May 18 and June 3, corresponding with three different steps of the reopening phase of the country, led by the government. Instagram posts are composed by several kind of languages: captions (texts), hashtags, emojis and images. The aim of this work is to design a set of procedures revealing the different polarity trends for each one and to propose a unique measure. This measure can show the sentiment expressed by the texts, in their semiotic broad meaning. The methodology proposed is based on a fully automatic natural language processing pipeline, including the images’ analysis phase. Its output is an interactive dashboard (Figure 1) that is able to explore the sentiment analysis values about every single kind of text, and the synthesis of all of them. Thanks to a system of interactions and filters, the observation is leaded by the images’ features, such as different kind of spaces (indoor or outdoor) and different kind of the photos’ subject (human or not human). The collected geographical data enabled the analysis of several dimensions, with an overview observation based on the regional scale. Hence, it gave us an opportunity to focus on the deeper level of the Italian provinces. This choice is motivated by the Italian DPCM (Decreto Presidenza del Consiglio dei Ministri) published on 24 March 20201, in which it is stated the partial autonomy of the regions.

Conclusion
Throughout the quantitative and qualitative analysis of the different expressive forms used on Instagram, this work proposes a general view of COVID-19 in Italy. The research brought together linguistic analysis and design into a more general semiotic framework. The aim was, in fact, to put in shape the pandemic phenomenon through a selection of linguistic relevance. The virus caused a series of unpredictable changes narrated on Instagram through the hashtag #andràtuttobene. A mantra for the Igers and an isotopy for the analysts (Greimas & Courtès 1979). Working on multiple levels, the research has offered a general and a local view of the emotions told during the lockdown period. Starting from a lexical base, made up by a list of words, and using electronic dictionaries also for the images, the analysis organized a large amount of data, developing a real map of emotions and needs expressed during the first wave of pandemic. The map can be visualized trough a dashboard letting users observe general and local reactions, down to the single province. The emotional effects of sense have been evaluated thanks to a polar and unique measure. In the end, did everything really go well for Instagram's Italy? In general, it seems so. The average sentiment value over the three days analyzed is always positive, with variations on regional and provincial scale. Going down the single province, we can find differences, as the Sardinia, Lazio and Liguria cases.