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

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.

Mental Health Analytics, Sentiment Analysis, Natural Language Processing, Emotion Detection

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