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Predicting life satisfaction based on the emotion words in self-statement texts.

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Summary

Researchers used machine learning models trained on emotion words from self-statements to predict life satisfaction. This approach offers a novel method for large-scale, real-time public mental health monitoring.

Keywords:
emotionlexiconlife satisfactionmachine learningself-statement

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Area of Science:

  • Computational Social Science
  • Psychology
  • Natural Language Processing

Background:

  • Real-time, large-scale measurement of life satisfaction is crucial for public mental health.
  • Traditional questionnaire methods have limitations for continuous, large-scale assessment.

Purpose of the Study:

  • To develop and validate machine learning models for predicting individual life satisfaction using emotion words from self-statements.
  • To explore the feasibility of online measurement of public life satisfaction.

Main Methods:

  • Utilized self-statement texts to extract emotion words.
  • Trained machine learning models, including Support Vector Regression (SVR), to predict life satisfaction scores.
  • Validated model performance using correlation with self-reported questionnaire scores and split-half reliability.

Main Results:

  • The SVR model demonstrated the best performance.
  • Achieved a correlation of 0.42 between predicted and self-reported life satisfaction scores.
  • Obtained a split-half reliability of 0.939, indicating high consistency.

Conclusions:

  • Identifying life satisfaction through emotional expressions in text is feasible.
  • This method provides a viable approach for online measurement of public life satisfaction.
  • Specific emotion categories like happiness, sorrow, and aversion are relevant indicators.