University of Twente Student Theses


Classification of Empathy and Call for Empathy in Child Help Forum Messages

Schoot Uiterkamp, L. (2021) Classification of Empathy and Call for Empathy in Child Help Forum Messages.

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Abstract:To improve automated detection of empathetic expressions and to streamline online discussion board moderation, an LSTM and a BERT neural network were trained to detect empathetic responses and calls for an empathetic response. Messages from the Kindertelefoon forum, labeled using crowd sourcing, were used as case study to provide a proof of concept. Assessing annotator reliability and determining reply relations were core considerations in cleaning the data. The BERT and LSTM models were trained on empathy detection and on call for empathy detection directly. The empathy detection models were also used in combination with a reply relation algorithm to predict call for empathy. Synthetic oversampling was used to counteract the class imbalance present in the data, as most messages did not contain an expression of empathy. The BERT model performed well in the empathy detection task, the LSTM model did not. The reply relation algorithm was not accurate and neither model performed well on the mediated call for empathy task. The BERT model again outperformed the LSTM model in direct call for empathy classification. The BERT models perform on par or better than neural networks implemented in empathy classification literature, the LSTM models perform significantly worse. The empathy classification and direct call for empathy classification models using BERT constitute a new state of the art in text-based empathy modelling and text-based emotion classification systems in general.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:17 linguistics and theory of literature, 54 computer science
Programme:Interaction Technology MSc (60030)
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