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We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns.

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Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen’s κ = 0.79). To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. The schemas underlying such thought records have, thus far, been largely manually identified.

To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The cognitive approach to psychotherapy aims to change patients’ maladaptive schemas, that is, overly negative views on themselves, the world, or the future.

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