Change in the embedding dimension as an indicator of an approaching transition.

Predicting a transition point in behavioral data should take into account the complexity of the signal being influenced by contextual factors. In this paper, we propose to analyze changes in the embedding dimension as contextual information indicating a proceeding transitive point, called OPtimal Embedding tRANsition Detection (OPERAND). Three texts were processed and translated to time-series of emotional polarity. It was found that changes in the embedding dimension proceeded transition points in the data. These preliminary results encourage further research into changes in the embedding dimension as generic markers of an approaching transition point.

Metaphor identification in large texts corpora.

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.

Proactive screening for depression through metaphorical and automatic text analysis.

OBJECTIVE: Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. MATERIALS AND METHOD: The system implementing the methodology--Pedesis--harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a "depression lexicon". The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic. RESULTS: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p<.001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall. CONCLUSION: Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.

A novel methodology for identifying emerging themes in small group dynamics.

An understanding of group dynamics, such as the dynamics in family psychotherapy, is of great importance to mental health practitioners and other experts who seek to understand the group as a working whole. In this context, identifying themes emerging from the dynamics is of prime importance. However, the lack of formal tools makes it difficult to validly identify emerging themes. The current article presents a novel methodology for identifying themes emerging from group dynamics. With this methodology, the verbal utterances of group members are automatically analyzed to produce a "group matrix." Motifs that emerge in the complex network of signs that is generated by the group are analyzed by means of a computer program, and the explanatory value of the identified motifs is elaborated. The methodology and its benefits are presented and illustrated through the analysis of (1) the family dynamics in a literary piece, Tennessee Williams's The Glass Menagerie and (2) the group dynamics of Israeli and Palestinian participants discussing the notion of forgiveness.

How language enables abstraction: a study in computational cultural psychology.

The idea that language mediates our thoughts and enables abstract cognition has been a key idea in socio-cultural psychology. However, it is not clear what mechanisms support this process of abstraction. Peirce argued that one mechanism by which language enables abstract thought is hypostatic abstraction, the process through which a predicate (e.g., dark) turns into an object (e.g., darkness). By using novel computational tools we tested Peirce's idea. Analysis of the data provides empirical support for Peirce's mechanism and evidence of the way the use of signs enables abstraction. These conclusions are supported by the in-depth analysis of two case studies concerning the abstraction of sweet and dark. The paper concludes by discussing the findings from a broad and integrative theoretical perspective and by pointing to computational cultural psychology as a promising perspective for addressing long-lasting questions of the field.