Machine learning, statistical data processing and applications using signal processing
(e.g. speech, image, video)
With the advancement of social artificial agents the need for correct understanding
of sentiment is growing. In this paper we propose a method for building a context-less
word-level emotional model of words in the Hungarian language based on Russell’s
Circumpex model of affect. By utilizing Bacterial Evolutionary Algorithm for feature
selection, a method for efficient web-based annotation is proposed. Using
the latent information of word embeddings multi-layer perceptron networks are trained
to realize an interpolative function of two-dimensional emotion vectors over
the embedding space. Dimensionality reduction via correlation analysis is
also discussed.
