December 3, Dipartimento di Informatica, Università di Torino. Felicittà. Visualizing and Estimating Happiness in

Transcription

1 : : Dipartimento di Informatica, Università di Torino December 3, 2013

2 : Outline

3 : from social media

4 Social media users generated contents represent the human behavior in everyday life, but... how to analyze data? how to connect quantitative research with theories that would qualitatively explain the observed and modeled phenomena? how to visualize data? statistical tools, machine learning techniques, large-scale network analysis and natural language modeling are hard to be applied by many skilled sociologists and communication scientists that do not want to deal directly with raw data or abstract models. :

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6 : An online platform for estimating happiness in Italian cities which provides visualization techniques to interactively explore the result of sentiment analysis performed over geotagged Tweets

7 is a fully implemented visualization system to estimate the level of happiness in a given geographical area based on geotagged Tweets, overlaying different analysis engines and visualization techniques a particular instantiation of the system, where a sentiment analysis engine for detecting Italian Tweets sentiment polarity has been developed and employed in order to estimate happiness in Italian cities and regions :

8 The framework includes: geotagged information retrieval APIs data enrichment with socio demographic information from statistics and other sources (e.g. ISTAT, Italian National Institute of Statistics) analysis of data producing an estimation of the general sentiment at different geographical and temporal scales visualization of analysis results :

9 : The framework

10 The analysis engine performs three main steps: pre-parsing: Tweets collection and cleaning by deletion or substitution of emoticons, links, mentions of other users and redundant punctuation parsing: Tweets morpho syntactic analysis and lemmatization (by Freeling) analysis: match of content words with MultiWordNet and WordNet Affect entries, aggregation of single words polarities, aggregation according to geolocation of Tweets polarities :

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12 techniques are adopted to support researchers and practitioners to explore the data about happiness in Italian cities. Three main views are implemented for describe happiness in regions, cities and top ten happiest places. An interactive interface where users can request for giving more contextual and quantitative details on maps, plots, tag clouds and other charts. :

13 the cities shows in the view Città for each day: a map of Italy a round marker for each town, which assumes different colors and sizes according to affective status and amount of posted messages an ordered score of the Italian towns details about the evaluation expressed by the system and Tweets posted there to test the reliability of the evaluation, for each city selected by the user :

14 : The view Città

15 : the regions shows in the view Regions for each day: a map of Italy where the regions are colored according to their affective status a score of the regions from the happiest to the less happy

16 : The view Regions

17 : Top ten happiest places shows in the view Top ten for each day: a map showing all the Tweets positioned within the area where they have been posted; by moving on the map, the user can zoom in to find the exact position of Tweets on the map and also to read them each Tweet is represented by a marker colored according to its detected affective polarity

18 : The view Top ten

19 Diachronic variation of happiness To observe how the level of happiness varies in time, offers a quarterly view: :

20 Diachronic variation of happiness... and a daily based view :

21 Clouds for happiness To observe the relations between happiness and words, can display tag-clouds, a visual representation useful for quickly perceiving the most prominent terms involved in analyzed Tweets. : Tag-cloud of the happiest august day in Turin

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23 Our first focus was on the sentiment visualization and summarization, but in order to improve we are working on other issues, in particular: to develop a sentiment analysis approach which goes beyond the simple lexicon-based one. For this purpose, we are developing a gold standard corpus of manually annotated Tweets to be used as a testbed for evaluation and comparison with other systems. to apply finer grained emotion detection techniques in order to classify Tweets according to different emotions (e.g. Ekman s basic emotions) or the emotional categories from the Plutchik s model to provide a sort of geography of emotions. :

24 : Thank you and...