Name
Title | ||
|---|---|---|
The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts. |
Abstract | ||
|---|---|---|
In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sourabh Zanwar | 1 | 0 | 0.68 |
| Daniel Wiechmann | 2 | 0 | 2.70 |
| Yu Qiao | 3 | 2267 | 152.01 |
| Elma Kerz | 4 | 0 | 3.38 |