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Affective interaction is a new emerging area of interest for interaction designers. This research explores the potential of our hybrid approach that relies on both, lexical and machine learning techniques for detection of Ekman's six emotional categories in user's text. The initial results of the performance evaluation of the proposed hybrid approach are encouraging and comparable to related research. A demonstrative mobile application that employs the proposed approach was developed to engage the users in a dialogue that solicits their reflections on various daily events and provides appropriate affective responses.

This paper reflects upon the challenges surrounding the efforts in recognizing and classifying user’s affective state. A suitable set of rules for contextual valence shifting has a central role in the proposed lexical-based approach for automatic emotion detection, which utilizes a diverse set of publicly available lexical resources. To evaluate the strengths and weaknesses of the embedded algorithm for word valence assignment, an experimental study with a suitable dataset was conducted and the performance results are discussed. A prototype multimodal mobile application that steers the conversational dialogue aligned with user’s affective states will also be presented.