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Politically correct. Sentiment analysis of Italian political texts

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My speech at the Data Driven Innovation 2019 in Rome

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Politically correct. Sentiment analysis of Italian political texts

  1. 1. POLITICALLY CORRECT SENTIMENT ANALYSIS OF ITALIAN POLITICAL TEXTS Roberto Reale, DDI 2019
  2. 2. The future offers very little hope for those who expect that our new mechanical slaves will offer us a world in which we may rest from thinking. — Norbert Wiener, God & Golem, Inc., 1964
  3. 3. ARTIFICIAL INTELLIGENCE AND POLITICS
  4. 4. MAJOR TOPICS  influence  governance  transparency  analytics
  5. 5. SENTIMENT ANALYSIS The use of computational methods to systematically identify, extract, quantify, and study affective states and subjective information.
  6. 6. TOOLS  natural language processing  text analysis  computational linguistics  biometrics
  7. 7. THE “MANIFESTO” PROJECT
  8. 8. THE MANIFESTO PROJECT  The Manifesto Project provides the scientific community with parties’ policy positions derived from a content analysis of parties’ electoral manifestos.  It covers over 1000 parties from 1945 until today in over 50 countries on five continents.
  9. 9. COLLECTIONS  Countries: Democratic countries, mostly member countries of the OECD.  Elections: Parliamentary (lower house) elections since the first democratic election in a country.  Parties: Programs of parties that gained at least one seat in parliament.  Documents: An authoritative document enacted and published by a party before an election that outlines a party’s policy plan for the time after the election and covers a broad range of policy issues.
  10. 10. TRAINING AND RULES  The coding (or annotation) is conducted by country experts.  The country expert coders are mostly political scientist or political science students and native speakers.
  11. 11. STRUCTURE OF THE MAIN DATASET  Each row in the dataset represents one electoral program.  The variables party and date jointly uniquely identify every row in the dataset.  It covers 4282 manifestos issued at 715 elections in 56 countries.
  12. 12. RES PUBLICA
  13. 13. DATA SETS  Italian Parliament  Manifesto texts of political parties
  14. 14. BOW VECTORIZATION  Segmentation into semantic units  Tokenization into Bag-of-Words vectors
  15. 15. A PROOF-OF-CONCEPT  A web app has been developed, as a fork and evolution of the “fipi” project.  Predicts political views of texts and newspaper articles.
  16. 16. A PROOF-OF-CONCEPT  Downloads, parses, and analyzes political articles from six major Italian newspapers on the whole political spectrum  Corriere della Sera  Il Fatto Quotidiano  il Giornale  Libero  la Repubblica  Il Sole 24 Ore
  17. 17. A PROOF-OF-CONCEPT  Based on Python (flask, scipy, scikit-learn, pandas and bs4), Docker and AWS Elasticbeanstalk.  Code available on GitHub
  18. 18. SOFTWARE  https://reale.me/respublica  https://github.com/reale/respublica
  19. 19. HTTPS://REALE.ME/RESPUBLICA
  20. 20. ROBERTO@REALE.ME

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