Abstract
In computational linguistics, binary sentiment analysis methods have been proposed to predict whether a document expresses a positive or a negative opinion. In this paper, we study a unique research problem—identifying environmental stimuli that contribute to different moods (mood triggers). Our analysis is enabled by an anonymous micro-journalling dataset, containing over 700,000 short journals from over 67,000 writers and their self-reported moods at the time of writing. We first build a multinomial logistic regression model to predict the mood (e.g., happy, sad, tired, productive) associated with a micro-journal. We then examine the model to identify predictive words and word trigrams associated with various moods. Our study offers new data-driven insights into public well-being.
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