ItEm: spazi semantici vettoriali per l'espansione semi-automatica di un lessico emotivo

In questo lavoro vengono utilizzati gli spazi semantici distribuzionali per ricercare dei termini emotivi, partendo da un nucleo ristretto di base, al fine di ampliare un lessico emotivo per la lingua italiana

ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon

In recent years computational linguistics has seen a rising interest in subjectivity, opinions, feelings and emotions. Even though great attention has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper, we present a methodology to build emotive lexicons by jointly exploiting vector space models and human annotation, and we provide the first results of the evaluation with a crowdsourcing experiment

The italian music superdiversity: Geography, emotion and language: one resource to find them, one resource to rule them all

Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices

The CoLing Lab system for Sentiment Polarity Classification of tweets

This paper describes the CoLing Lab system for the EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. Our system is based on a SVM classifier trained on the rich set of lexical, global and twitter-specific features described in these pages. Overall, our system reached a 0.63 weighted F-score on the test set provided by the task organizers

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Superdiversity: (Big) Data analytics at the crossroads of geography, language and emotions

In a series of articles, Vertovec focused on the changes and contexts that have affected migratory flows around the world. These demographic changes, which Vertovec defines Superdiversity, are the result of the globalisation and they outline a change in the overall level of migration patterns. Over time, the migration routes have increased both their diversity and complexity. The nature of immigration has brought with it a transformative ``diversification of diversity''. Strictly connected with ethnicity and Superdiversity studies, the phenomenon of human migration has been a constant during human history. In the era of Big Data, every single user lives in a hyper-connected world. More than 75\% of the world's population has a mobile phone, and over half of these are smartphones. The use of social media grows together with the number of connected people. In these \emph{social} Big data, User-Generated Content incorporate a high number of discriminating information. Language, space and time are three of the best features that can be employed to detect Superdiversity. The strongest point of social Big Data is that they typically natively include various information about different dimensions. Starting from these observations, in this thesis, we define a measure of Superdiversity, a Superdiversity Index, by adding the emotional dimension and placing it in the context of social Big Data. Our measure is based on an epidemic spreading algorithm that is able to automatically extend the dictionary used in lexicon-based sentiment analysis. It is easily applicable to various languages and suitable for Big Data. Our Superdiversity Index allows for comparing diversity from the point of view of the emotional content of language in different communities. An important characteristic of our Superdiversity Index is the high correlation with immigration rates. For this reason, we believe this can be used as an essential feature in a nowcasting model of migration stocks. Our framework can be applied with higher time and space resolution compared to official statistics. Moreover, we apply our method to a different context and data to measure the Superdiversity of the music world