Learn More
This paper presents a novel approach to the estimation of user’s affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the(More)
This paper presents a novel method for estimating speaker’s affective states based on two contextual features: valence shifters and appropriateness. Firstly, a system for affect analysis is used to recognise specific types of emotions. We improve the baseline system with the analysis of Contextual Valence Shifters (CVS), which determine the semantic(More)
By our demonstration we want to introduce our achievements in combining different purpose algorithms to build a chatbot which is able to keep a conversation on any topic. It uses snippets of Internet search results to stay within a context, Nakamura's Emotion Dictionary to detect an emotional load existence and categorization of a textual utterance and a(More)
This paper presents a method for estimating contextual appropriateness of speaker’s emotions, supported with the analysis of Contextual Valence Shifters (CVS), which determine the semantic orientation of the valence of emotive expressions. In the proposed method a Web mining technique is used to verify the contextual appropriateness of the emotions(More)
  • 1