STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Algorithms Design and Analysis Review Original Research

Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification

  1. Aditi Biswas Department of Computer Science and Engineering, JECRC University, Jaipur
  2. Surendra Kumar Yadav Department of Computer Science and Engineering, JECRC University, Jaipur
  3. Shruti Mathur Department of Computer Science and Engineering, JECRC University, Jaipur

Abstract

Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to analyze posts from mental health forums. The proposed architecture integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings to capture nuanced semantic relationships within text, along with a multi-channel convolutional neural network enhanced by residual connections. This hybrid structure effectively teaches both local phrase-level features and broader contextual patterns, improving classification performance across diverse emotional expressions. To ensure transparency and trustworthiness, especially important in sensitive domains such as mental health, we incorporate a gradient × embedding explainability mechanism. This component highlights token-level contributions toward each predicted emotion category, enabling users to understand which specific words most influenced the model’s decision. The framework was evaluated on a benchmark mental health dataset consisting of seven emotion classes: joy, sadness, anger, fear, love, surprise, and others. Experimental results demonstrate an overall classification accuracy of 92%. Additionally, the visualization of influential words enhances interpretability, thereby supporting responsible and explainable AI-driven sentiment analysis in real-world mental health applications.

Keywords

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