Summary of Sentiment Informed Sentence Bert-ensemble Algorithm For Depression Detection, by Bayode Ogunleye et al.
Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection
by Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Applications (stat.AP)
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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 paper investigates machine learning (ML) techniques for early-stage depression detection using social media datasets. Despite existing studies’ limitations in dealing with data complexities, prone to overfitting, and generalization issues, the authors examine the performance of multiple ML algorithms. They incorporate sentiment indicators to improve model performance and find that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into a stacking ensemble model achieve comparable F1 scores on two benchmark datasets. The results suggest using sentiment indicators as an additional feature for depression detection yields improved model performance, recommending the development of a depressive term corpus for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding new ways to use machine learning to detect depression early on by looking at social media data. Right now, there are limited studies on this topic and the current methods aren’t very good because they don’t handle complex data well and tend to get stuck in their own patterns. To fix this, the authors test different machine learning algorithms and add something called sentiment indicators to make them better. They find that using a special type of algorithm called SBERT with an ensemble model does pretty well on two big datasets. The results suggest that adding more information about how people feel can help detect depression earlier. |
Keywords
» Artificial intelligence » Encoder » Ensemble model » Generalization » Machine learning » Overfitting
