Felicittà: Visualizing and Estimating Happiness in Italian Cities from Geotagged Tweets

Felicitta 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, Felicitta 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.

[1]  Huan Liu,et al.  Twitter Data Analytics , 2013, SpringerBriefs in Computer Science.

[2]  Cristina Bosco,et al.  Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT , 2013, IEEE Intelligent Systems.

[3]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[4]  Alessandro Rozza,et al.  Modelling political disaffection from Twitter data , 2013, WISDOM '13.

[5]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[6]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[7]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[8]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[9]  Nancy Ide,et al.  Distant Supervision for Emotion Classification with Discrete Binary Values , 2013, CICLing.

[10]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[11]  Christopher M. Danforth,et al.  The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place , 2013, PloS one.

[12]  C. Fellbaum An Electronic Lexical Database , 1998 .

[13]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[14]  Malvina Nissim,et al.  Sentiment analysis on Italian tweets , 2013, WASSA@NAACL-HLT.

[15]  Filippo Menczer,et al.  Visualizing Communication on Social Media: Making Big Data Accessible , 2012, ArXiv.

[16]  Sanda M. Harabagiu,et al.  EmpaTweet: Annotating and Detecting Emotions on Twitter , 2012, LREC.