Artificial intelligence and content analysis

Cuilenburg, J.J. van; Kleinnijenhuis, J.; Ridder, J.A. de

doi:10.1007/bf00430638

Cited by 30 publications

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“…The primary distinction among these relational approaches to analyzing texts is between those based on semantic grammars (semantic text analysis) versus those based on networks (network text analysis). Network text analysis has been used to compare mental maps (Carley, 1986(Carley, , 1988 or to evaluate performative networks among political policy makers (Van Cuilenburg et al, 1988). This article introduces a semantic text analytical approach to the study of political discourse within the democratizing societies of Central and Eastern Europe.…”

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Semantic text analysis and the measurement of ideological developments within fledgling democracies

Popping

Roberts

2014

Social Science Information

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This methodological article presents an introduction to the field of clause-based semantic text analysis. The method is introduced and elaborated with regard to the study of ideological developments within fledgling democracies. In such studies modality plays an important role. Democratic societies are maintained in accordance with either a modality of achievement or one of necessity. This is illustrated using editorial texts from Hungary, one of the countries in Central and Eastern Europe where people had to find their democratic way after communism disappeared in 1989.

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confidence: 99%

Semantic text analysis and the measurement of ideological developments within fledgling democracies

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Roberts

2014

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“…If so, one might have to distinguish two concepts: "skills" and "essential skills". This way of coding resembles very much the ways followed by other investigators in the field of network text analysis (Carley, 1993;Van Cuilenburg et al, 1988).…”

Section: Coding a Clausementioning

confidence: 93%

Text Analysis for Knowledge Graphs

Popping

2006

Qual Quant

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The concept of knowledge graphs is introduced as a method to represent the state of the art in a specific scientific discipline. Next the text analysis part in the construction of such graphs is considered. Here the ‘translation’ from text to graph takes place. The method that is used here is compared to methods used in other approaches in which texts are analysed. Copyright Springer Science + Business Media B.V. 2007text analysis, knowledge graphs,

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“…Further, they require far more refined theories of text production and understanding. This makes it inherently more difficult for human coders to process messages reliably, and even harder to automatize using computers (van Cuilenburg et al, 1988). Historically, statistical approaches have been the dominant paradigm in ACA, and even with the recent developments in computer linguistics, syntactic-semantic approaches are quite rare in applied communication research (van Atteveldt, 2008, is a notable exception).…”

Section: Typology Of Approaches To Acamentioning

confidence: 99%

“…At the same time, with advances in artificial intelligence research, approaches beyond dictionary coding and co-occurrence analysis were rediscovered. The initial enthusiasm of Weber (1984, p. 142) and others, however, was dulled by the realization that computers would not be able to understand texts in the foreseeable future (van Cuilenburg, Kleinnijenhuis, & de Ridder, 1988). Nevertheless social scientists, most notably Dutch researchers from the CETA project, began to investigate the possibilities of syntactic-semantic content analysis with computer assistance, with the objective of moving beyond word counts and purely statistical approaches of text analysis.…”

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Content Analysis, Automatic

Scharkow¹

2017

The International Encyclopedia of Communication Research Methods

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Automatic content analysis (ACA) is a technique for coding messages with the help of computer algorithms. Unlike computer-aided content analysis, ACA is defined as any method in which the actual coding decision, that is, assigning codes to documents or single textual or audiovisual elements, does not require human judgment and therefore is performed automatically. Since ACA relies on the computing capabilities of machines rather than human coders, it can be applied to very large documents. Moreover, automatic coding is highly reliable in that any analysis can be reproduced exactly given the same material and software and all errors are deterministic, that is, stemming from misspecification or program errors. Historical developmentThe history and development of ACA can be understood through three central themes: (i) the concept of content analysis and its computational implementation, (ii) the development of software for automatic data processing and analysis, and (iii) the provision and analysis of digital or machine-readable documents. The first phase of development of computer-assisted and automatic content analysis in the late 1950s was mainly characterized by experiments with the computer. For this, social scientists had to first learn to program the computer, and in most cases, this involved the support of the science departments. The first few experiments were almost exclusively limited to producing text statistics, that is, counting words, a technique that had been applied in many disciplines such as political science or literature since the 1920s (Holsti, 1969;Stone, Dunphy, Smith, & Ogilvie, 1966). Conceptually, these simple automatic analyses fell well behind the advancements made in conventional content analysis by Lasswell, Lerner, and de Sola Pool (1952) or Osgood (1959). However, early content analysts were increasingly troubled by the costs of manual coding and had high expectations from automatic approaches, the development of which was viewed as central to the success of the method (Stone, 1997). At this time, computer-assisted content analysis was a high-risk research domain, plagued with numerous problems. On the one hand, primitive computing hardware and software allowed only small amounts of text to be analyzed with a few variables (Iker & Harway, 1965). On the other hand, because of the dearth of machine-readable documents, all units of analysis had to be digitized in a complex and error-prone process on punch cards. Thus, in the early 1960s, ACA was neither cost-effective-half an

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Artificial intelligence and content analysis

Cited by 30 publications

References 6 publications

Semantic text analysis and the measurement of ideological developments within fledgling democracies

Semantic text analysis and the measurement of ideological developments within fledgling democracies

Text Analysis for Knowledge Graphs

Content Analysis, Automatic

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