In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users’ influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.
Twitter Vigilance: a Multi-User platform for Cross-Domain Twitter Data Analytics, NLP and Sentiment Analysis / Cenni, Daniele; Nesi, Paolo; Pantaleo, Gianni; Zaza, Imad. - ELETTRONICO. - (2017), pp. 1-8. (Intervento presentato al convegno First IEEE International Conference on Smart City Innovations (IEEE SCI 2017) tenutosi a San Francisco Bay Area, USA nel 4-8 August 2017).
Twitter Vigilance: a Multi-User platform for Cross-Domain Twitter Data Analytics, NLP and Sentiment Analysis
daniele cenni;paolo nesi
;gianni pantaleo;imad zaza
2017
Abstract
In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users’ influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.
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