Beyond Sentiment: The Manifold of Human Emotions
This work addresses the need for richer emotion modeling in natural language processing, though it appears incremental by extending existing sentiment analysis with a continuous approach.
The paper tackled the problem of representing human emotions in text beyond binary sentiment by proposing a continuous manifold model, resulting in significant improvements over a baseline without the manifold and enabling visualization of positive sentiment variations across domains.
Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.