Depression Level Detection Using AI

This project focuses on using the BERT algorithm to stumble on despair tiers in social media threads. We preprocess textual data from social media bills, adapt BERT for despair detection, and combine the version into a person-friendly interface. BERT analyzes linguistic nuances, sentiment, and contextual cues to evaluate the severity of depressive symptoms in the threads. The model is trained on annotated datasets with various despair levels, and its performance is evaluated using metrics together with precision, recollect, and F1-score. The assignment ambitions to make a contribution to AI programs in mental health, supplying a tool for early detection and assist inside the context of various language used on social media.

Authors

Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/icisaa62385.2024.10828795
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Depression Level Detection Using AI

Sheetal Pawar, M. Venkatesan, Priyanka Thorat
Emotion and Mood Recognition
article

Depression Level Detection Using AI

Sheetal Pawar, M. Venkatesan, Priyanka Thorat
article en

Abstract

This project focuses on using the BERT algorithm to stumble on despair tiers in social media threads. We preprocess textual data from social media bills, adapt BERT for despair detection, and combine the version into a person-friendly interface. BERT analyzes linguistic nuances, sentiment, and contextual cues to evaluate the severity of depressive symptoms in the threads. The model is trained on annotated datasets with various despair levels, and its performance is evaluated using metrics together with precision, recollect, and F1-score. The assignment ambitions to make a contribution to AI programs in mental health, supplying a tool for early detection and assist inside the context of various language used on social media.

Openalex Percentile: Top 68%
Emotion and Mood Recognition
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