Mental Health Sentiment Analytics Dashboard: Temporal Pattern Analysis and Mood Forecasting Using NLP

Abstract

Mental health disorders — depression, anxiety, and stress — have surged in global prevalence, creating urgent demand for automated assessment tools that can operate at scale. Natural Language Processing (NLP) offers a compelling path forward: it can analyse large volumes of informal text, from social media posts to chatbot conversations, and surface linguistic patterns that correlate with psychological distress. This paper reviews NLP-based methods for mental health sentiment analysis and frames them within a conceptual architecture for a Mental Health Sentiment Analytics Dashboard — a unified system that integrates sentiment inference, temporal pattern analysis, and mood forecasting into a single, clinician-facing interface. Rather than describing a working implementation, the paper synthesises the literature across four analytical dimensions: text representation, learning paradigms, temporal modelling strategies, and real-world application domains. A consolidated comparative table covering major NLP approaches and benchmark datasets is provided to enable side-by-side evaluation. The paper also critically examines the ethical tensions, methodological gaps, and deployment challenges that must be resolved before such systems can be responsibly integrated into clinical practice.