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Improving responsiveness of public services in housing by monitoring social media impact

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Copyright Bojan Cestnik at CeDEM14

Copyright Bojan Cestnik at CeDEM14


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  • 1. 1/26 INTRODUCTION: • Motivation • Sentiment analysis • Desire: Include reason and emotion in social media and PR analysis METHOD AND RESULTS: • Method workflow • Used formulas • Results on a case study CONCLUSION AND FURTHER WORK TALK OVERVIEW
  • 2. 2/26 PROBLEM: • Competent managing of public relations requires substantial amount of resources and skill • Expert intuition might not always be accurate • D. Kahneman: Thinking, Fast and Slow • Every inappropriate response might cause a „tsunami“ effect of media inquiries and/or public activities • Cyprus crisis in March 2013: the Dutch Finance Minister Dijsselbloem said: „The Cyprus deal will be used as a template for the future solutions of similar Eurozone banking problems“ MOTIVATION I
  • 3. 3/26 SOCIAL NETWORKS (APHORISMS BY NOSHIR CONTRACTOR): • Social networks: • It‘s not what you know, it‘s who you know. • Cognitive social networks: • It‘s not who you know, it‘s who they think you know. • Knowledge networks: • It‘s not who you know, it‘s what they think you know. • Cognitive knowledge networks: • It‘s not who you know, it‘s what who you know knows. MOTIVATION II
  • 4. 4/26 GOAL: • Support a process of managing public relation within an e-gov organization with a sentiment analysis technology RELATED EXAMPLES: • Presidential election in 2012 in Slovenia (emotions from Twitter) • Monitoring the influence of emotions in the press to financial markets (EU project First) MOTIVATION III
  • 5. 5/26 EMOTIONS FROM TWITTER
  • 6. 6/26 FACEBOOK (MARCH 2014): • 1,28 billion monthly active users • Average user has 130 friends • 802 million users log in every day TWITTER (APRIL 2014): • About a billion members • 255 million monthly active users (77% outside US) • 100 million daily active users • 500 million tweets sent every day • Average user has 208 followers SOCIAL MEDIA STATISTICS
  • 7. 7/26 SENTIMENT ANALYSIS: • Rational arguments constitute foundations of science, economics and law • Emotions put flavor to our everyday lives in politics and business • Explanatory models based on reason alone often fail to account for the complexity of reality • An attempt to overcome such limitations by combining rational models and emotional explanatory approach resulted in a new method called sentiment analysis • Sentiment analysis aims to automatically elicit emotions like happy-sad or positive-neutral-negative from fragments of text INTRODUCTION I
  • 8. 8/26 INTENTION
  • 9. 9/26 SENTIMENT ANALYSIS IMPLEMENTED: • Simple sentiments: positive and negative • More complex sentiments: • joy, surprise, anger, disgust, fear, sadness • Difficulty: language used in social media • Relatively low accuracy of sentiment classification • Sentiment analysis still useful on a large scale INTRODUCTION II
  • 10. 10/26 GOAL: • Support a process of managing public relation within an e-gov organization with a sentiment analysis technology METHOD OVERVIEW: • Analysis of user posts to a forum • Workflow that includes receiving questions from media, generating answers, storing and analyzing textual data CASE STUDY: • Sentiment of user posts to forum • Archive of journalists‘ questions and answers in the period between October 2007 and November 2012 METHOD
  • 11. 11/26 METHOD WORKFLOW
  • 12. 12/26 METHOD 1 WORKFLOW Monitor news from press and broadcasting media Monitor social media posts Articles Posts Tagged data News articles, broadcasts Posts to forums, Twitter, Facebook Responses Analyse media and prepare responses Data storage
  • 13. 13/26 THE HOUSING FUND OF THE REPUBLIC OF SLOVENIA: • Founded in 1991 • Offer loans under favorable terms to citizens • Encourage savings in housing • Build, sell and rent apartments • Past project: offer housing subventions to young families IMPORTANT SLOVENIAN PUBLIC INSTITUTION: • Considerable media attention FINANCIAL FIGURES: • 429 M€ assets • 125 M€ in long term loans to citizens THE CLIENT
  • 14. 14/26 USED DATASETS: • Training dataset: 345 preselected short questions in Slovene language containing negative, neutral and positive wording • Testing dataset: • 298 journalists’ questions and answers in the period between October 2007 and November 2012 • 103 press releases, 41 explanations, and 8 press conferences • 296 posts to the forum from March 2010 till October 2013 FORMULAS: • Word frequencies and conditional probabilities of emotion states AVERAGE SENTIMENT: • Workflow that includes receiving questions from media, generating answers, storing and analyzing textual data RESULTS
  • 15. 15/26 TRAINING: TESTING: COMBINATION: S = ROUND ( P(☺) * 7 + P( ) * 4 + P( ) * 1) – 4 FORMULAS
  • 16. 16/26 SENTIMENT PROBABILITIES FOR GIVEN WORDS word w p(☺☺☺☺ | w) p( | w) p( | w) advantrage 0,50 0,31 0,19 efficient 0,54 0,01 0,45 kind 0,55 0,30 0,15 ... blame 0,20 0,01 0,79 angry 0,19 0,00 0,80 reject 0,11 0,07 0,82 ... saving 0,45 0,54 0,01 good 0,30 0,56 0,14 return 0,28 0,64 0,08
  • 17. 17/26 PRESS AND FORUM SENTIMENT COMPARISON
  • 18. 18/26 AVERAGE SENTIMENT
  • 19. 19/26 SENTIMENT IN FINANCE
  • 20. 20/26 SENTIMENT IN DNEVNIK
  • 21. 21/26 SENTIMENT IN DELO
  • 22. 22/26 SENTIMENT IN RTV SLO
  • 23. 23/26 SENTIMENT BY THE NUMBER OF SUB- QUESTIONS
  • 24. 24/26 PICTURE TAKEN BY A CONCERNED
  • 25. 25/26 RESULTS HIGHLIGHTS • Approach to agile sentiment analysis used at the Housing Fund • Following the sentiment in social networks and user forums • Officers can validate their intuitive ideas with the analysis‘ results • Consequence of the analysis: More frequent and regular press conferences FURTHER WORK • Improve the user interface to speed-up the decision process • Extend the analysis to other social media sources like Twitter and Facebook CONCLUSION & FURTHER WORK
  • 26. 26/26 SENTIMENT AND DAYS TO ANSWER
  • 27. alenka.kern@ssrs.si, bojan.cestnik@temida.si