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Department of Communication Science, VU University Amsterdam Semantic NETwork analysis Manual and automatic content analysis.

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Presentation on theme: "Department of Communication Science, VU University Amsterdam Semantic NETwork analysis Manual and automatic content analysis."— Presentation transcript:

1 Department of Communication Science, VU University Amsterdam Semantic NETwork analysis Manual and automatic content analysis of Source : agent / predicate / target relationships Jan Kleinnijenhuis / Wouter van Atteveldt Atteveldt

2 Semantic Network Analysis 2 Department of Communication Science The Network Institute, VU University Amsterdam Topics 1.Introduction Semantic NETwork Content Analysis 2.Human coding, using CETA, iNET 3.Automatic coding 1.Extraction of source: agent /(predicate) / target quadruples 2.Sentiment Analysis: (predicate)  association.. dissocation 4.Discussion: extraction of issue positions 2

3 Semantic Network Analysis 3 Department of Communication Science The Network Institute, VU University Amsterdam Introduction Semantic NETwork Content Analysis 3 1

4 Semantic Network Analysis 4 Department of Communication Science The Network Institute, VU University Amsterdam Background o subject / predicate / object o Early Greeks, both semantic and syntactic, agent / predicate / target o Namen gleichen Punkten, Sätzen Pfeilen (1921) o xRy propositions, Ludwig Wittgenstein (Tractatus 3.144) o Evaluative assertion analysis (1956) o Heider (1946) balance theory => cognitive consistency theory o Charles Osgood, Nunnally Saporta (1956) o Automatic content analysis, (co)occurrence keywords (1960s -..) o Stone e.a. (1965), The General Inquirer o Efficient indexing algorithms, e.g. Google, Lucene o Semantic Network Analysis, relational content analysis (1980s-..) o Van Cuilenburg e.a. 1986, deRidder, 1994, Kleinnijenhuis e.a. 1997, 2001, Van Atteveldt, forthcoming; also labeled as NET-method o Semantic Web (1990s - …), xRy + logic => inferences

5 Semantic Network Analysis 5 Department of Communication Science The Network Institute, VU University Amsterdam Definitions key concepts 1 o (meaning) object  entity o actor, issue, Ideal or UnspecifiedReality,  Actor  animated, e.g. person, group, organization  Issue  non-animated, e.g. employment, health care  Ideal, value  criterion for evaluation actor or issue, e.g. referent of entranching in “Obama’s smile is entranching”)  unspecifiedReality, e.g. referent of it in “it’s lucky for Bush”) o appearing as subjects (  agents) and/or objects (  targets, recipients) in texts o ontology o A priori knowledge of relationships between meaning objects  Politian  Person  Actor; Politician[period]  Party  Actor; politician[period]  PolFunction  BarackObama  Democrats [1994..?]; BarrackObama  PresCandidate[2007..?] o operationalized with an ontology dictionary:  set of (linguistically or statistically enhanced) queries to search for occurrence of separate meaning objects in natural language

6 Semantic Network Analysis 6 Department of Communication Science The Network Institute, VU University Amsterdam Definition Semantic NETwork Analysis1 Extraction from texts of source: agent / predicate / target- quadruples so as to infer conclusions from their network representation o subject  agent  {actors, issues; default=unspecifiedReality} = meaning object directing action or energy as described by the o predicate  {dissociations.. associations}  Thesaurus: (eventually context specific) synsets of words / mwu’s whose conjugations and combinations amount predictably to a value on the dissociation..association-scale (e.g. cooperate  +1; bomb  -1) o towards the object  target  recipient  {actors, issues; default= Ideal} o according to a (quoted or paraphrased) source  {actors; default=author} (cf. R.M.W. Dixon, 1992, A new approach to English grammar, on semantic principles)

7 Semantic Network Analysis 7 Department of Communication Science The Network Institute, VU University Amsterdam NET relation types with prototype examples

8 Semantic Network Analysis 8 Department of Communication Science The Network Institute, VU University Amsterdam Issue positions: often ends, means and causal expectation in 1 sentence Het CDA gaat door met ingrepen in de zorg om de overheidsuitgaven te beperken o Issue position, means CDA / gaat door met (+) / ingrepen in de zorg o Issue position, end CDA / wil beperken (-) / overheidsuitgaven o Causal relationship CDA: ingrepen in de zorg / om te beperken (-) / overheidsuitgaven

9 Semantic Network Analysis 9 Department of Communication Science The Network Institute, VU University Amsterdam Human coding, using CETA / iNET 9 2

10 Semantic Network Analysis 10 Department of Communication Science The Network Institute, VU University Amsterdam NET by human coders using CETA (Jan A, de Ridder), iNET (Wouter van Atteveldt)

11 Semantic Network Analysis 11 Department of Communication Science The Network Institute, VU University Amsterdam SNA by human coders using INET, ontology lookup

12 Semantic Network Analysis 12 Department of Communication Science The Network Institute, VU University Amsterdam SNA by human coders using INET, network lookup

13 Semantic Network Analysis 13 Department of Communication Science The Network Institute, VU University Amsterdam SNA by human coders using INET, 3 more networks

14 Semantic Network Analysis 14 Department of Communication Science The Network Institute, VU University Amsterdam Automatic coding. Source: subject /pred/object-extraction

15 Semantic Network Analysis 15 Department of Communication Science The Network Institute, VU University Amsterdam Tools ontology construction: co-occurrence analysis Amos Tversky (1977): features of similarity Islam*, terror* and immig* in NRC, AD

16 Semantic Network Analysis 16 Department of Communication Science The Network Institute, VU University Amsterdam Tools ontology 2: syntactic profiling BOLKESTEIN Werkwoorden waarmee Bolkestein als lijdend voorwerp geassocieerd is: kapittel, beticht, haal uit naar, zet af tegen, besta tussen, beweer, sla aan, citeer, typeer, kritiseer, beschuldig, waarschuw, bedien, verwijt, vergelijk, val aan, leg voor aan, roep, verras, overtuig Werkwoorden waarmee Bolkestein als onderwerp geassocieerd is: schop-in de war, moraliseer, zoek aan, matig, scherts, overspeel, zwengel aan, verkwansel, nuanceer, snoer- de mond, herroep, kom-in botsing, zwalk, neem terug, krab, trek-van leer, vier feest, maak- korte metten, opteer, belijd Bijvoeglijke naamwoorden waarmee Bolkestein geassocieerd is: negentiende-eeuws BRINKMAN Werkwoorden waarmee Brinkman als lijdend voorwerp geassocieerd is: licht-beentje, sta- terzijde, tik-op de vingers, fluit terug, reken aan, stem op, ondervraag, interview, adviseer, wijs aan, schrijf af, prijs aan, eer, corrigeer, sta bij, kritiseer, houd-in de gaten, confronteer, schuif, spreek aan Werkwoorden waarmee Brinkman als onderwerp geassocieerd is: diskwalificeer, paai, bijt vast, nuanceer, volhard, draag mee, sta-te woord, bezin, baal, profileer, blijf aan, leun, herzie, poseer, speculeer, beraad, leg neer, bid, heb-de tijd, betreur Bijvoeglijke naamwoorden waarmee Brinkman geassocieerd is: gereformeerd, kil, arm, ander © Gosse Bouma, RUG

17 Semantic Network Analysis 17 Department of Communication Science The Network Institute, VU University Amsterdam Automation NET

18 Semantic Network Analysis 18 Department of Communication Science The Network Institute, VU University Amsterdam Alpino-tree ==> source: subject / pred / object

19 Semantic Network Analysis 19 Department of Communication Science The Network Institute, VU University Amsterdam Concurrent validity extraction Source: subject / (pred )/ object

20 Semantic Network Analysis 20 Department of Communication Science The Network Institute, VU University Amsterdam Predictive validity, Sources and Subjects (acting actors)

21 Semantic Network Analysis 21 Department of Communication Science The Network Institute, VU University Amsterdam Automatic coding. Sentiment analysis  assoc.. dissoc

22 Semantic Network Analysis 22 Department of Communication Science The Network Institute, VU University Amsterdam Sentiment analysis: decomposition F1 performance

23 Semantic Network Analysis 23 Department of Communication Science The Network Institute, VU University Amsterdam F1 per relation type, elections 2006 manual corpus

24 Semantic Network Analysis 24 Department of Communication Science The Network Institute, VU University Amsterdam Sentiment analysis: aggregate performance 2006 campaign

25 Semantic Network Analysis 25 Department of Communication Science The Network Institute, VU University Amsterdam Extraction aggregate issue positions, 2006 campaign

26 Semantic Network Analysis 26 Department of Communication Science The Network Institute, VU University Amsterdam Relative performance 2006 campaign full ‘grammar’ model Cell entries represent correlation coefficients with manual content analysis

27 Semantic Network Analysis 27 Department of Communication Science The Network Institute, VU University Amsterdam Discussion: Content Analysis of Issue Positions 27 4

28 Semantic Network Analysis 28 Department of Communication Science The Network Institute, VU University Amsterdam Automatic extraction of Issue Positions presupposes o Manual codings (machine learning; validity tests) o Ontology of meaning objects (actors, issues, values, reality) o Ontology dictionary o Linguistic preprocessing: o tokenization, lemmatizing, parsing  syntax graph o e.g. Van Noord, Bouma : ALPINO o Identification o Syntax graph + rules  semantic roles of source, agent, target o Semantic roles + ontology dictionary + anaphora resolution + posthoc extraction  source:agent/pred(assoc..dissoc)/target o Sentiment analysis  pred(assoc..dissoc)

29 Semantic Network Analysis 29 Department of Communication Science The Network Institute, VU University Amsterdam Discussion: prospects for advance o Ontology, ontology dictionary o e.g. more subissues, context-specific synonyms o Rules to transform syntax graph  semantic roles o e.g. rules dealing with different modifier types o sentiment analysis o e.g. multi word unit-recognition; informed features o Combining rule-based with statistical approaches (e.g. LSA) starting from high-order linguistic features o Error analysis o e.g. more specific validity tests starting from manual coding o Other languages.. new language domains..

30 Semantic Network Analysis 30 Department of Communication Science The Network Institute, VU University Amsterdam Literature


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