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.
Highlights
In the psychological sense, happiness is a state of mind that can be typically defined in terms of life satisfaction, pleasure, or positive emotional conditions
The Shared Task was focused on using the HappyDB corpus for analyzing happiness and well-being in written language via the accomplishment of two sub-tasks: (1) Using a small labeled and large unlabeled training data to predict the two thematic labels “agency” and “sociality”, where agency examines whether a particular emotion stems from the individual, is inspired by other individuals, or results solely from the situation, while sociality refers to whether or not people other than the author are involved in the emotion situation; (2) Developing interesting ways for automatic characterization of the happy moments in terms of affect, emotion, participants and content
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, namely, gender, parenthood status, marital status, and age group
Summary
Happiness is a state of mind that can be typically defined in terms of life satisfaction, pleasure, or positive emotional conditions. Happiness can be seen in the sense of well-being. In this sense, hedonists define happiness as the experience of pleasure and desire theorists define happiness in terms of obtaining one’s desires
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