Juha Raitio

Subjects on objects in contexts: Using GICA method to quantify epistemological subjectivity

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 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 size subjects × objects × contexts. Through flattening into a matrix, these subject-object-context (SOC) tensors can again be analyzed using various 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, GICA 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.

Quantifying the effect of meaning variation in survey analysis

Surveys are widely conducted as a means to obtain information on thoughts, opinions and feelings of people. The representativeness of a sample is a major concern in using surveys. In this article, we consider meaning variation which is another potentially remarkable but less studied source of problems. We use Grounded Intersubjective Concept Analysis (GICA) method to quantify meaning variation and demonstrate the effect on survey analysis through a case study in which food prices and food concepts are considered.

Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings

Surveys are widely conducted as a means to obtain information on thoughts, opinions and feelings of people. The representativeness of a sample is a major concern in using surveys. In this article, we consider meaning variation which is another potentially remarkable but less studied source of problems. We use Grounded Intersubjective Concept Analysis (GICA) method to quantify meaning variation and demonstrate the effect on survey analysis through a case study in which food prices and food concepts are considered.

Hybrid bilinear and trilinear models for exploratory analysis of three-way poisson counts

We propose a probabilistic model class for the analysis of three-way count data, motivated by studying the subjectivity of language. Our models are applicable for instance to a data tensor of how many times each subject used each term in each context, thus revealing individual variation in natural language use. As our main goal is exploratory analysis, we propose hybrid bilinear and trilinear models with zero-mean constraints, separating modeling the simpler and more complex phenomena. While helping exploratory analysis, this approach leads into a more involved model selection problem. Our solution by forward selection guided by cross-validation likelihood is shown to work reliably on experiments with synthetic data.

Identifying anomalous social contexts from mobile proximity data using binomial mixture models

Mobile proximity information provides a rich and detailed view into the social interactions of mobile phone users, allowing novel empirical studies of human behavior and context-aware applications. In this study, we apply a statistical anomaly detection method based on multivariate binomial mixture models to mobile proximity data from 106 users. The method detects days when a persons social context is unexpected, and it provides a clustering of days based on the contexts. We present a detailed analysis regarding one user, identifying days with anomalous contexts, and potential reasons for the anomalies. We also study the overall anomalousness of peoples social contexts. This analysis reveals a clear weekly oscillation in the predictability of the contexts and a weekend-like behavior on public holidays.

Combining Self-Organizing and Bayesian Models of Concept Formation

In this article, we consider contemporary theories of concepts, and Bayesian and self-organizing models of concept formation. After introducing the different models, we present our own experiment. It utilizes a multi-agent simulation framework, in which the emergence of a common vocabulary can be studied. In the experiment, we use jointly the self-organizing maps and probabilistic modeling of concept naming. The results of the experiments show that a common vocabulary to denote prototypical colors emerges in the agent population.