Hybrid models for opinion analysis in speech interactions

Sentiment analysis is a trendy domain of Machine Learning which has developed considerably in the last several years. Nevertheless, most of the sentiment analysis systems are general. They do not take profit of the interactional context and all of the possibilities that it brings. My PhD thesis focuses on creating a system that uses the information transmitted between two speakers in order to analyze opinion inside a human-human or a human-agent interaction. This paper outlines a research plan for investigating a system that analyzes opinion in speech interactions, using hybrid discriminative models. We present the state of the art in our domain, then we discuss our prior research in the area and the preliminary results we obtained. Finally, we conclude with the future perspectives we want to explore during the rest of this PhD work.