Emotion recognition: A smoothed Dirichlet multinomial solution

Multinomial-based models have been extensively used for count data modeling and challenging applications such as image processing, text recognition, and behavioral sciences. Despite the good performance obtained with those models, they still suffer from challenging issues that require continuous exploring of other alternative approaches. In this work, we address the issue of smoothing language modeling. To the best of our knowledge, distributions defined in a smoothed simplex were not considered before as conjugate priors for the multinomial. We propose a smoothed Dirichlet multinomial (SDM) distribution and a mixture of SDMs with a likelihood-based learning. We evaluate the proposed approach on three challenging applications related to emotion recognition: depression on social media, happiness analysis, and pain estimation. The smoothed Dirichlet multinomial solution presents the best results comparing to the related works and the multinomial-based models such as Dirichlet compound multinomial and the multinomial model.