Alle Publikationen
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2016
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(2016): Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation, S. 155-163
Abstract: We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet’s sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.
2015
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(2015): Emotionally expressive dynamic physical behaviors in robots. In: International Journal of Human-Computer Studies 78, S. 1-16. DOI: 10.1016/j.ijhcs.2015.01.006
DOI: http://www.sciencedirect.com/science/article/pii/S1071581915000191 Abstract: For social robots to respond to humans in an appropriate manner, they need to use apt affect displays, revealing underlying emotional intelligence. We present an artificial emotional intelligence system for robots, with both a generative and a perceptual aspect. On the generative side, we explore the expressive capabilities of an abstract, faceless, creature-like robot, with very few degrees of freedom, lacking both facial expressions and the complex humanoid design found often in emotionally expressive robots. We validate our system in a series of experiments: in one study, we find an advantage in classification for animated vs static affect expressions and advantages in valence and arousal estimation and personal preference ratings for both animated vs static and physical vs on-screen expressions. In a second experiment, we show that our parametrically generated expression variables correlate with the intended user affect perception. Combining the generative system with a perceptual component of natural language sentiment analysis, we show in a third experiment that our automatically generated affect responses cause participants to show signs of increased engagement and enjoyment compared with arbitrarily chosen comparable motion parameters.
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