1. Social Psychological Analysis of Public Political Comments on Facebook
Márton Miháltz

2. TrendMiner Overview
What kind of social political trends are there in Hungarian comments to political posts on Facebook?
Facebook in Hungary: 4.27M registered users = 59.2% of internet users, 43% of total population
Download all public comments from Hungarian politicians’, parties’ facebook pages
Analysis of comments:
Basic NLP (tokenization, PoS, stemming), domain-adapted
Entities: political actors (people, organizations)
Sentiment
Social psychology dimensions: agency/communion, individualism/collectivism, optimism/pessimism, primordial/conceptual thinking
In cooperation with Narrative Psychology Research Group, Hungarian Academy of Sciences

3. Data Acquisition Get comments via fb Graph API
1.9M comments for 141K fb posts ( – )
from 1344 fb pages
Organizations: parties, regional and associated branches
People: candidate and elected representatives (MPs), government, party officials
Official and fan pages
In 3 categories
Hungarian parliament
Hungarian parliament elections 2014 (6th April)
EU parliament elections 2014 (25th May)
Sources: valasztas.hu, wikipedia.hu
Everything in a MySQL database
For arbitrary queries (political groups, time etc.)

4. Data model Fb_pages Fb_posts, Fb_comments Comments_annotations
Id, URL, Page title
Type: person or organization
Affiliated party (3 campaigns)
Fb_posts, Fb_comments
Id, Created_timestamp
Message text, Author_user_id
Comments_annotations
Sentence_id, Start_token, End_token index
Annotated text, Lemmatized_annotated_text, Annotation_tag
Fb_comments_scores
16 scores and counts (sentiment, RID,, agency, communion, optimism, …)

5. Hungarian Political Ontology
Extending TM multilingual political ontology
8 New classes, 3+3 new object/data properties, new instances (1 Country,18 Party, 661 Politician, 899 Nomination)
Nominated and elected MPs (2010 Hu. Parl., 2014 Hu. Parl., 2014 EU Parl.), nominating parties;
Names, abbreviated names, nicknames, Facebook page URLs etc.
Example:

6. Hungarian Political Ontology
Example: Benedek Jávor was member of Hungarian Parliament during (nominated by LMP), member of European Parliament from 2014 (nominated by EGYÜTT-PM).

7. Processing Pipeline Downloading (Fb Graph API py script)
Tokenizaton (huntoken tool)
PoS-tagging (hunmorph tool)
Morphological analysis (hunmorph tool)
Stem+analysis disambiguation (Python script)
Content analysis (Java NooJ)
Scoring & storage in DB
Uploading in RDF to TM Integration Server

8. Domain Adaptation
Problem: existing NLP tools developed on different domain, (f)ail on social media language (facebook comments)
Using corpus for survey:
1.25M fb comments (29M tokens)
2.25M unknown tokens (694K types)
Frequency list, f > 15 items manually revised
Identify common problems
Lists of frequent, relevant unknown, new words etc.

9. Domain Adaptation: Tokenization
Huntoken tool
Frequent problems:
missing spaces around punctuation ... end of sentence.Beginning of another ...
Multiplicated punctuation first part……. Second part
Contracted words (slang) asszem = azt hiszem (“I think”)
Consonant multiplication (interjections, onomatopeic words etc.) e.g. pfffffffff, uffffff, ejjjjjjjj (pff(f*), uff(f*), ej(j*))
split large numbers by decimal groups
split URLS
split emoticons : D

10. Domain Adaptation: PoS/stemming
Hunpos tagger + hunmorph analyzer + stemming script
Frequent problems:
Unknown words (no lemma/PoS)
add to hunmorph analyzer’s lexicon
using analogous words (morphological paradigm)
Compounds, abbreviations, acronyms, slang words etc.
Frequently misspelled word forms:
replace with correct forms
Wrong capitalization e.g. SENTENCES IN ALL CAPS
Missing accent characters –disambiguation model needed E.g. kor (age), kór (disease), kör (circle)

11. NooJ, Java NooJ, Nooj-cmd
Open source version of NooJ: define and run finite state machines for querying, annotation etc. (morphology, syntax)
NooJ-Cmd extension: all NooJ GUI features => command line options
Open source:
NooJ grammars (FSMs) for annotation:
Actors (entities)
Emotional valence (sentiment polarity)
Regressive imagery dictionary
Agency-communion
Optimism-pessimism
Individualism-collectivism

12. Development of NooJ Grammars
In collaboration with social psychologist researchers
Social Psychology Department, Eötvös Lóránd University, Budapest
Narrative Psychology Research Group, Hungarian Academy of Sciences
Development Corpus
176K sample fb comments from 570 fb pages (4.9M tokens)
NLP annotation
Frequency lists (lemmas, lemmas+PoS, lemmas+morphological info etc.)
Development:
f > 100 content words from development corpus (3500 types)
7 independent annotators
>= 4 annotartors agree: manual revision
Compile into NooJ grammar with polarity shifters, items to be excluded etc.

13. 1. Political Actors (NEs)
Maxent NE tool (huntag): low performance on domain
Trained on standard language news texts
Miscategorization, false positive NEs, entity boundary recognition problems
NooJ grammar/lexicon for Trendminer
Person names: family_name (given_name_lemmatized)? | frequent_nicknames …
Organization names: Standard_form | abbreviated_forms… | nicknames…
Created automatically (names from DB) + manually (nicknames from freq. lists)

14. 2. Emotional Valence Emotions with positive or negative polarity
Polarity in context: recognize negation using simple rules
Nouns, adjectives, verbs, adverbs, emoticons, multi-word expressions
500 Positive, 420 negative entries

15. 3. Regressive Imagery Dictionary
Martindale (1975, 1990): uncover psychological processes reflected in the text
2 basic categories of thinking:
Primordial (primary): associative, concrete, and takes little account of reality (fantasy, dreams)
Conceptual (secondary): abstract, logical, reality oriented, aimed at problem solving
7+29 more subcategories (social behavior, cognition, perceptions, sensations etc.)
Hungarian version by Pólya and Szász
3000+ terms

16. 4. Agency/Communion 2 fundamental dimensions of social values:
Communion: moral and emotional aspects of an individual’s relations to others (affection, expressiveness, cooperation, social benefit etc.)
Agency: efficiency of an individual’s goal-orientated behavior (motivation, competence, control)
Positive or negative for both dimensions
Context dependent (e.g. negation)
640 expressions

17. 5. Optimism/Pessimism
Based on PoS and morphology annotations + time expressions
2 measures: 1. |future_tense_verbs| / (|present_tense_verbs| + |past_tense_verbs|) 2. |present_tense_verbs| / |past_tense_verbs|
Both correlate with degree of optimism

18. 6. Individualism/Collectivism
Based on PoS and morphology annotations
1 measure: |personal pronouns| / (|verbs with personal inflection| + |nouns with possessive inflection|)
Higher score: higher degree of individualism

19. Visualisation

24. Dissemination and Exploitation
Presentations
Hungarian NLP Meetup, Sept , Budapest
conText, Nov , Budapest
Conference papers, presentations
2 papers at 11th Conference on Hungarian Computational Linguistics (January , Szeged)
Source code
Project website (
Download political ontology
Download 1.9M facebook comments corpus (w/ annotations)
Project info, papers, presentations slides

25. Thank You!