Hybrid approach to detecting symptoms of depression in social media entries.

Authors

Agnieszka Wołk, Polish-Japanese Academy of Information TechnologyFollow
Karol Chlasta, SWPS University of Social Sciences and HumanitiesFollow
Paweł Holas, University of WarsawFollow

Paper Type

FP

Paper Number

137

Abstract

Sentiment and lexical analyses are widely used to detect depression or anxiety disorders. It has been documented that there are significant differences in the language used by a person with emotional disorders in comparison to a healthy individual. Still, the effectiveness of these lexical approaches could be improved further because the current analysis focuses on what the social media entries are about, and not how they are written. In this study, we focus on aspects in which these short texts are similar to each other, and how they were created. We present an innovative approach to the depression screening problem by applying Collgram analysis, which is a known effective method of obtaining linguistic information from texts. We compare these results with sentiment analysis based on the BERT architecture. Finally, we create a hybrid model achieving a diagnostic accuracy of 71%.

Recommended Citation

Wołk, Agnieszka; Chlasta, Karol; and Holas, Paweł, "Hybrid approach to detecting symptoms of depression in social media entries." (2021). PACIS 2021 Proceedings. 192.
https://aisel.aisnet.org/pacis2021/192