Monte Carlo Tree Search for Interpreting Stress in Natural Language
This addresses the need for interpretable mental health analysis in NLP, though it is incremental as it builds on existing classification models.
The paper tackled the problem of explaining why a person is experiencing a mental state from text, such as stress in Reddit posts, by developing a Monte Carlo tree search method that identifies key phrases for interpretable explanations in both context-dependent and context-independent ways.
Natural language processing can facilitate the analysis of a person's mental state from text they have written. Previous studies have developed models that can predict whether a person is experiencing a mental health condition from social media posts with high accuracy. Yet, these models cannot explain why the person is experiencing a particular mental state. In this work, we present a new method for explaining a person's mental state from text using Monte Carlo tree search (MCTS). Our MCTS algorithm employs trained classification models to guide the search for key phrases that explain the writer's mental state in a concise, interpretable manner. Furthermore, our algorithm can find both explanations that depend on the particular context of the text (e.g., a recent breakup) and those that are context-independent. Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.