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Micro-journal mining to understand mood triggers

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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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Notes

  1. TF-IDF is the frequency of a given word in a given journal divided by the logarithm of the fraction of journals this word appears in.

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  1. University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Liuyan Chen & Lukasz Golab

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  1. Liuyan Chen
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  2. Lukasz Golab
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Correspondence to Lukasz Golab.

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Chen, L., Golab, L. Micro-journal mining to understand mood triggers. Computing 102, 1227–1244 (2020). https://doi.org/10.1007/s00607-019-00777-6

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Keywords

Mathematics Subject Classification

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