Felicittà is an online platform for estimating happiness in the Italian cities, which uses Twitter as data source and combines sentiment analysis and visualization techniques in order to provide users with an interactive interface for data exploration. In particular, Felicittà daily analyzes Twitter posts and exploits temporal and geo-spatial information related to Tweets in order to easy the summarization of sentiment analysis outcomes and the exploration of the Twitter data. By interactive maps it provides users with the possibility to have a comprehensive overview of the sentiment analysis results about the main Italian cities, and with the opportunity to zoom-in to a specific region to visualize a fine-grained map of the city or district as well as the location of the individual sentiment-labeled Tweets. The platform allow users to tune their view on such huge amount of information and to interactively reduce the inherent complexity, possibly providing an hint for finding meaningful patterns, and correlations between moods and events.

Felicittà

RUFFO, Giancarlo Francesco;PATTI, Viviana
2013-01-01

Abstract

Felicittà is an online platform for estimating happiness in the Italian cities, which uses Twitter as data source and combines sentiment analysis and visualization techniques in order to provide users with an interactive interface for data exploration. In particular, Felicittà daily analyzes Twitter posts and exploits temporal and geo-spatial information related to Tweets in order to easy the summarization of sentiment analysis outcomes and the exploration of the Twitter data. By interactive maps it provides users with the possibility to have a comprehensive overview of the sentiment analysis results about the main Italian cities, and with the opportunity to zoom-in to a specific region to visualize a fine-grained map of the city or district as well as the location of the individual sentiment-labeled Tweets. The platform allow users to tune their view on such huge amount of information and to interactively reduce the inherent complexity, possibly providing an hint for finding meaningful patterns, and correlations between moods and events.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11579/145016
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