Using topic modeling to infer the emotional state of people living with Parkinson's disease.

Using topic modeling to infer the emotional state of people living with Parkinson's disease. Assist Technol. 2019 Jun 13;:1-10 Authors: Valenti AP, Chita-Tegmark M, Tickle-Degnen L, Bock AW, Scheutz MJ Abstract Individuals with Parkinson's disease (PD) often exhibit facial masking (hypomimia), which causes reduced facial expressiveness. This can make it difficult for those who interact with the person to correctly read their emotional state and can lead to problematic social and therapeutic interactions. In this article, we develop a probabilistic model for an assistive device, which can automatically infer the emotional state of a person with PD using the topics that arise during the course of a conversation. We envision that the model can be situated in a device that could monitor the emotional content of the interaction between the caregiver and a person living with PD, providing feedback to the caregiver in order to correct their immediate and perhaps incorrect impressions arising from a reliance on facial expressions. We compare and contrast two approaches: using the Latent Dirichlet Allocation (LDA) generative model as the basis for an unsupervised learning tool, and using a human-crafted sentiment analysis tool, the Linguistic Inquiry and Word Count (LIWC). We evaluated both approaches using standard machine learning performance metrics such as precision, recall, and F1 scores. Our performance analysis of the two approaches s...
Source: Assistive Technology - Category: Rehabilitation Tags: Assist Technol Source Type: research