Summary of Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories, by Jinwen Tang et al.
Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories
by Jinwen Tang, Qiming Guo, Yunxin Zhao, Yi Shang
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study bridges the gap in analyzing social media text data for identifying linguistic features indicative of mental health issues by utilizing a dataset sourced from Reddit, focusing on Expressive Narrative Stories (ENS) from individuals with and without self-declared depression. It evaluates the utility of advanced language models, BERT and MentalBERT, against traditional models. The research finds that traditional models are sensitive to the absence of explicit topic-related words, whereas BERT exhibits minimal sensitivity to the absence of topic words in ENS, suggesting its superior capability to understand deeper linguistic features. Both models excel at recognizing linguistic nuances and maintaining classification accuracy even when narrative order is disrupted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores social media text data for mental health insights by analyzing Expressive Narrative Stories (ENS) from individuals with and without depression. Researchers used two advanced language models, BERT and MentalBERT, to see how well they could identify mental health issues in ENS. The results show that traditional models are good at recognizing specific words related to mental health, but struggle when those words are missing. In contrast, BERT is better at understanding deeper meanings in the stories, even when the order of sentences is changed. |
Keywords
» Artificial intelligence » Bert » Classification
