Automatic Emotional Text Annotation Using Facial Expression Analysis

Sentiment analysis calls for large amounts of annotated data, which are usually in short supply and require great efforts in terms of manual annotation. Furthermore, the analysis is often limited to text polarity and writer’s emotions are ignored, even if they provide valuable information about writer's feelings that might support a large number of applications, such as open innovation processes. Our research is hence aimed at developing a methodology for the automatic annotation of texts with regard to their emotional aspects, exploiting the correlation between speech and facial expressions in videos. In the present work we describe the main ideas of our proposal, presenting a four-phase methodology and discussing the main issues related to the selection of input frames and the processing of emotions resulting from facial expressions analysis.