ArticleJMIR mental health2022
In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Brief speech samples reveal emotional states in daily life.PNAS nexus · 2026Article
- Grandchild contact and grandparents' positive and negative emotions in daily life: an analysis using self-report and language use data.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026Article
- Affective language and transdiagnostic symptom burden in clinically referred adolescents: a multicenter Turkish sentence-completion study.Frontiers in psychology · 2026Article
- The construction of emotional meaning in language.Communications psychology · 2025Review
- Language measures correlate with other measures used to study emotion.Communications psychology · 2025Article
- Emotional Granularity is Associated with Daily Experiential Diversity.Affective science · 2023Article
- Some Recommendations on the Use of Daily Life Methods in Affective Science.Affective science · 2022Article
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8 authors.
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Abstract
backgroundEmotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language.
objectiveThe aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression.
methodsIn a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation.
resultsOverall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion-language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance.
conclusionsAlthough using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R
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