Enhancing Clinical Sentiment Analysis with a Novel Stochastic Model Based on Brownian Motion

Résumé

Depression profoundly affects emotional states, behaviors, and overall quality of life. In the digital era, patients increasingly share their experiences through online comments, offering valuable yet complex emotional narratives. However, traditional sentiment analysis methods, which often assign a single emotional label to an entire text, fail to capture the nuanced intra-comment emotional fluctuations, overlooking the evolving and sometimes contradictory nature of emotional expression. This study introduces a novel sentiment analysis framework that models intra-comment emotional flow as a Brownian-like trajectory. Inspired by the random motion of particles in physics, the proposed method treats each sentence as a time step and assigns it a sentiment polarity score. The cumulative sequence of these scores constructs an emotional curve that reflects the dynamic affective progression throughout the comment, effectively simulating a Brownian path. From these trajectories, quantitative indicators—including drift, variance, -signal changes, trajectory length ( ), maximum emotional level ( ), minimum emotional level ( ), and amplitude — are extracted, providing a detailed and structured understanding of emotional instability. To uncover patterns across comments, the K-Means clustering algorithm is applied, enabling the automatic grouping of comments with similar emotional dynamics. In addition, Principal Component Analysis (PCA) is used to reduce dimensionality and facilitate the visualization and interpretation of emotional profiles. The proposed approach is applied to a corpus of comments from depressed patients to identify characteristic emotional patterns and examine differences according to the type of treatment. This work highlights the relevance of stochastic modeling for advancing sentiment analysis in complex psychological contexts and offers a tool to support remote monitoring, personalized interventions, and early detection of emotional relapse.

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