Therapeutic Change Process Research (TCPR) connects within-therapeutic change processes to outcomes. The labour intensity of qualitative methods limit their use to small scale studies. Automated text-analyses (e.g. text mining) provide means for analysing large scale text patterns. We aimed to provide an overview of the frequently used qualitative text-based TCPR methods and assess the extent to which these methods are reliable and valid, and have potential for automation. We systematically reviewed PsycINFO, Scopus, and Web of Science to identify articles concerning change processes and text or language. We evaluated the reliability and validity based on replicability, the availability of code books, training data and inter-rater reliability, and evaluated the potential for automation based on the example- and rule-based approach. From 318 articles we identified four often used methods: Innovative Moments Coding Scheme, the Narrative Process Coding Scheme, Assimilation of Problematic Experiences Scale, and Conversation Analysis. The reliability and validity of the first three is sufficient to hold promise for automation. While some text features (content, grammar) lend themselves for automation through a rule-based approach, it should be possible to automate higher order constructs (e.g. schemas) when sufficient annotated data for an example-based approach are available.

Abstract. In everyday life, people frequently talk to others to help them deal with negative emotions. To some extent, everybody is capable of comforting other people, but so far conversational agents are unable to deal with this type of situation. To provide intelligent agents with the capability to give emotional support, we propose a domain-independent conversational model that is based on topics suggested by cognitive appraisal theories of emotion and the 5-phase model that is used to structure online counseling conversations. The model is implemented in an embodied conversational agent called Robin.

Abstract. Recently, we proposed a dialogue model for social support. To validate this model, we analyzed 23 real world chat conversations. After some adjustments, the dialogue sequence patterns specified in the model cover 87.4% of the data. Based on this result, we conclude that the dialogue model accurately describes comforting conversations. Next, the model will be incorporated into a comforting ECA.

No abstract

Complexity of event data in texts makes it difficult to assess its content, especially when considering larger collections in which different sources report on the same or similar situations. We present a system that makes it possible to visually analyze complex event and emotion data extracted from texts. We show that we can abstract from different data models for events and emotions to a single data model that can show the complex relations in four dimensions. The visualization has been applied to analyze 1) dynamic developments in how people both conceive and express emotions in theater plays and 2) how stories are told from the perspective of their sources based on rich event data extracted from news or biographies.

No abstract

Humanities scholars agree that the visualization of their data should bring order and insight, reveal patterns and provide leads for new research questions. However, simple two-dimensional visualizations are often too static and too generic to meet these needs. Visualization tools for the humanities should be able to deal with the observer dependency, heterogeneity, uncertainty and provenance of data and the complexity of humanities research questions. They should furthermore offer scholars the opportunity to interactively manipulate their data sets and queries. In this paper, we introduce Storyteller, an open source visualization tool designed to interactively explore complex data sets for the humanities. We present the tool, and demonstrate its applicability in three very different humanities projects.

No abstract