Emotions in text: dimensional and categorical models

TLDR: A new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches is introduced and shows that the categorical model using NMF and the dimensional model tend to perform best.

Abstract: Text often expresses the writer's emotional state or evokes emotions in the reader. The nature of emotional phenomena like reading and writing can be interpreted in different ways and represented with different computational models. Affective computing (AC) researchers often use a categorical model in which text data are associated with emotional labels. We introduce a new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches. The approach is evaluated using four data sets of texts reflecting different emotional phenomena. An emotional thesaurus and a bag-of-words model are used to generate vectors for each pseudo-document, then for the categorical models three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). For the dimensional model a normative database is used to produce three-dimensional vectors (valence, arousal, dominance) for each pseudo-document. This three-dimensional model can be used to generate psychologically driven visualizations. Both models can be used for affect detection based on distances amongst categories and pseudo-documents. Experiments show that the categorical model using NMF and the dimensional model tend to perform best.