Subjects on Objects in Contexts : Using GICA Method to Quantify Epistemological Subjectivity

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Honkela , T , Raitio , J , Lagus , K , Nieminen , I T , Honkela , N & Pantzar , M 2012 , Subjects on Objects in Contexts : Using GICA Method to Quantify Epistemological Subjectivity . in The 2012 International Joint Conference on Neural Networks (IJCNN) : Brisbane, Australia (June 10-15, 2012) . IEEE , Piscataway, NJ , pp. 2875-2883 , IEEE World Congress on Computational Intelligence , Brisbane , Australia , 10/06/2012 . https://doi.org/10.1109/IJCNN.2012.6252765

Title: Subjects on Objects in Contexts : Using GICA Method to Quantify Epistemological Subjectivity
Author: Honkela, Timo; Raitio, Juha; Lagus, Krista; Nieminen, Ilari T.; Honkela, Nina; Pantzar, Mika
Contributor: University of Helsinki, Aalto University
University of Helsinki, Aalto University
University of Helsinki, Department of Social Research (2010-2017)
University of Helsinki, National Consumer Research Center
Publisher: IEEE
Date: 2012
Number of pages: 9
Belongs to series: The 2012 International Joint Conference on Neural Networks (IJCNN) Brisbane, Australia (June 10-15, 2012)
ISBN: 978-1-4673-1488-6
978-1-4673-1490-9
URI: http://hdl.handle.net/10138/318069
Abstract: A substantial amount of subjectivity is involved in how people use language and conceptualize the world. Computational methods and formal representations of knowledge usually neglect this kind of individual variation. We have developed a novel method, Grounded Intersubjective Concept Analysis (GICA), for the analysis and visualization of individual differences in language use and conceptualization. The GICA method first employs a conceptual survey or a text mining step to elicit to elicit from varied groups of individuals the particular ways in which terms and associated concepts are used among the individuals. The subsequent analysis and visualization reveals potential underlying groupings of subjects, objects and contexts. One way of viewing the GICA method is to compare it with the traditional word space models. In the word space models, such as latent semantic analysis (LSA), statistical analysis of word-context matrices reveals latent information. A common approach is to analyze term-document matrices in the analysis. The GICA method extends the basic idea of the traditional term-document matrix analysis to include a third dimension of different individuals. This leads to a formation of a third-order tensor of dimension subjectobjectcontexts. Through flattening, these subject-object-context (SOC) tensors can be analyzed using different computational methods including principal component analysis (PCA), singular value decomposition (SVD), independent component analysis (ICA) or any existing or future method suitable for analyzing high-dimensional data sets. In order to demonstrate the use of the GICA method, we present the results of two case studies. In the first case, a GICA analysis of health-related concepts is conducted. In the second one, the State of the Union addresses by US presidents are analyzed. In these case studies, we apply multidimensional scaling (MDS), the self-organizing map (SOM) and Neighborhood Retrieval Visualizer (NeRV) as specific data analysis methods within the overall GICA method. The GICA method can be used, for instance, to support education of heterogeneous audiences, public planning processes and participatory design, conflict resolution, environmental problem solving, interprofessional and interdisciplinary communication, product development processes, mergers of organizations, and building enhanced knowledge representations in semantic web.
Subject: 113 Computer and information sciences
text analysis
data mining
self-organizing semantic map
PRINCIPAL COMPONENT ANALYSIS
singular value decomposition
independent component analysis
knowledge representation
Tensor factorization
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