In this paper, we evaluate the effectiveness of emotion and sentiment based representations for the task of building machine learning models for FR detection.
we illustrate that improved data representations can be achieved by leveraging a plurality of emotion and sentiment extraction methods, as well as by estimating emotions and sentiments on a part-by-part basis by segmenting the reviews.