  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.
