Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Bosubabu Sambana, Kondreddygari Archana, Suram Indhra Sena Reddy, Shaik Meethaigar Jameer Basha, Shaik Karishma

TL;DR
This paper introduces an AI-powered system that uses advanced language models to analyze social media content for negative emotions and cognitive distortions, aiding early mental health intervention within the CBT framework.
Contribution
It presents a novel integration of multiple language models for sentiment analysis, summarization, and translation to detect mental health issues from social media data.
Findings
Effective detection of negative emotions and cognitive distortions
Prediction of potential mental health disorders like phobias and eating disorders
Enhanced early intervention capabilities for psychotherapists
Abstract
Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analyze these cognitive pathways, which could be critical for psychotherapists aiming to deliver timely and effective interventions in online environments. Cognitive Behavioral Therapy (CBT) framework leveraging acceptance, commitment and data augmentation to categorize and address both textual and visual content as positive or negative. Specifically, the system employs BERT, RoBERTa for Sentiment…
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