Copyright Bojan Cestnik at CeDEM14

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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

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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

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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

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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

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EMOTIONS FROM TWITTER

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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

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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

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INTENTION

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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

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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

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METHOD WORKFLOW

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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

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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

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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

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TRAINING:
TESTING:
COMBINATION:
S = ROUND ( P(☺) * 7 + P( ) * 4 + P( ) * 1) – 4
FORMULAS

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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

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PRESS AND FORUM SENTIMENT
COMPARISON

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AVERAGE SENTIMENT

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SENTIMENT IN FINANCE

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SENTIMENT IN DNEVNIK

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SENTIMENT IN DELO

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SENTIMENT IN RTV SLO

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SENTIMENT BY THE NUMBER OF SUB-
QUESTIONS

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PICTURE TAKEN BY A CONCERNED

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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

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SENTIMENT AND DAYS TO ANSWER

28. alenka.kern@ssrs.si, bojan.cestnik@temida.si