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Computing Happiness from Textual Data

Author

Listed:
  • Emad Mohamed

    (Research Group in Computational Linguistics, University of Wolverhampton, Wolverhampton WV1 1LY, UK
    These authors contributed equally to this work.)

  • Sayed A. Mostafa

    (Department of Mathematics & Statistics, North Carolina A&T State University, Greensboro, NC 27411, USA
    These authors contributed equally to this work.)

Abstract

In this paper, we use a corpus of about 100,000 happy moments written by people of different genders, marital statuses, parenthood statuses, and ages to explore the following questions: Are there differences between men and women, married and unmarried individuals, parents and non-parents, and people of different age groups in terms of their causes of happiness and how they express happiness? Can gender, marital status, parenthood status and/or age be predicted from textual data expressing happiness? The first question is tackled in two steps: first, we transform the happy moments into a set of topics, lemmas, part of speech sequences, and dependency relations; then, we use each set as predictors in multi-variable binary and multinomial logistic regressions to rank these predictors in terms of their influence on each outcome variable (gender, marital status, parenthood status and age). For the prediction task, we use character, lexical, grammatical, semantic, and syntactic features in a machine learning document classification approach. The classification algorithms used include logistic regression, gradient boosting, and fastText. Our results show that textual data expressing moments of happiness can be quite beneficial in understanding the “causes of happiness” for different social groups, and that social characteristics like gender, marital status, parenthood status, and, to some extent age, can be successfully predicted form such textual data. This research aims to bring together elements from philosophy and psychology to be examined by computational corpus linguistics methods in a way that promotes the use of Natural Language Processing for the Humanities.

Suggested Citation

  • Emad Mohamed & Sayed A. Mostafa, 2019. "Computing Happiness from Textual Data," Stats, MDPI, vol. 2(3), pages 1-24, July.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:3:p:25-370:d:245372
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    2. Ruut Veenhoven, 2015. "Informed Pursuit of Happiness: What we should know, do know and can get to know," Journal of Happiness Studies, Springer, vol. 16(4), pages 1035-1071, August.
    3. Sofie Vanassche & Gray Swicegood & Koen Matthijs, 2013. "Marriage and Children as a Key to Happiness? Cross-National Differences in the Effects of Marital Status and Children on Well-Being," Journal of Happiness Studies, Springer, vol. 14(2), pages 501-524, April.
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