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Explore what people think about PA. A case study. Cristina Menghini, Collaboratrice del Team per la Trasformazione Digitale

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Data Driven Government 2. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma tre

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Explore what people think about PA. A case study. Cristina Menghini, Collaboratrice del Team per la Trasformazione Digitale

  1. 1. Explore what people think about PA A case study Cristina Menghini - Collaboratrice presso il Team per la Trasformazione Digitale cricri.menghini@gmail.com
  2. 2. Continuous flow of news that trigger citizens reactions Free to express and share our opinion! Exploit the information on the web!
  3. 3. Obiettivi ● Catch impression and sentiment (positive o negative) of citizens w.r.t specific topics and events. ● Discover information that belong to the same topic Let the PA act taking into account the public opinion. How? We provide a tool to navigate the information BUT...
  4. 4. Watch out! Social networks do not offer a complete view of population opinion. But still they can provide good insights ● Data is not from sampling techniques ● Put particular attention when you make conclusions
  5. 5. Exploit Twitter Data Collection Twitter streaming API Collector personalised according to the PA interests. ➔ Hashtag ➔ Account Data Analysis ★ Split tweets per topic ★ Summarise information about topics and hashtags Data Viz Visualizzazione dei risultati ★ Graph of tweets ★ Identified topics ★ Dashboard ★ Sentiment Analysis
  6. 6. Data Analysis Cluster tweets Build up the graph G: ● Nodes: hashtags {h1 , …, hn } ● Edges: connetto hi con hj se compaiono nello stesso tweet Represent hashtag in a space s.t. we can measure their similarity. ◆ Hierarchical clustering Hashtag proxy del contenuto del tweet Topic insieme di hashtag Tweet “soft”-assigned to the clusters
  7. 7. Data Analysis Sentiment Analysis Insieme di tweet, reviews etichettati - Vettorizzazione dei testi - Word Embedding - Per ogni parola, variabile che la indica come positiva o negativa secondo vocabolario controllato Model - Convolutional Neural Network 73% of F1 score, we can do better! (RNNs) Classification problem: - Positive, Neutral or Negative?
  8. 8. Demo visualizzazione

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