In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study
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: lan...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2022-02-01
|
| Series: | JMIR Mental Health |
| Online Access: | https://mental.jmir.org/2022/2/e31724 |
| _version_ | 1827858607001042944 |
|---|---|
| author | Chiara Carlier Koen Niemeijer Merijn Mestdagh Michael Bauwens Peter Vanbrabant Luc Geurts Toon van Waterschoot Peter Kuppens |
| author_facet | Chiara Carlier Koen Niemeijer Merijn Mestdagh Michael Bauwens Peter Vanbrabant Luc Geurts Toon van Waterschoot Peter Kuppens |
| author_sort | Chiara Carlier |
| collection | DOAJ |
| description |
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 R2 values are low. |
| first_indexed | 2024-03-12T12:57:36Z |
| format | Article |
| id | doaj.art-7bdff9b7a427489fb3b857d7470c40e3 |
| institution | Directory Open Access Journal |
| issn | 2368-7959 |
| language | English |
| last_indexed | 2024-03-12T12:57:36Z |
| publishDate | 2022-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Mental Health |
| spelling | doaj.art-7bdff9b7a427489fb3b857d7470c40e32023-08-28T20:47:06ZengJMIR PublicationsJMIR Mental Health2368-79592022-02-0192e3172410.2196/31724In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field StudyChiara Carlierhttps://orcid.org/0000-0003-4985-6240Koen Niemeijerhttps://orcid.org/0000-0002-0816-534XMerijn Mestdaghhttps://orcid.org/0000-0001-5077-861XMichael Bauwenshttps://orcid.org/0000-0002-6340-7516Peter Vanbrabanthttps://orcid.org/0000-0003-0217-3303Luc Geurtshttps://orcid.org/0000-0002-9608-9147Toon van Waterschoothttps://orcid.org/0000-0002-6323-7350Peter Kuppenshttps://orcid.org/0000-0002-2363-2356 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 R2 values are low.https://mental.jmir.org/2022/2/e31724 |
| spellingShingle | Chiara Carlier Koen Niemeijer Merijn Mestdagh Michael Bauwens Peter Vanbrabant Luc Geurts Toon van Waterschoot Peter Kuppens In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study JMIR Mental Health |
| title | In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study |
| title_full | In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study |
| title_fullStr | In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study |
| title_full_unstemmed | In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study |
| title_short | In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study |
| title_sort | in search of state and trait emotion markers in mobile sensed language field study |
| url | https://mental.jmir.org/2022/2/e31724 |
| work_keys_str_mv | AT chiaracarlier insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT koenniemeijer insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT merijnmestdagh insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT michaelbauwens insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT petervanbrabant insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT lucgeurts insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT toonvanwaterschoot insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy AT peterkuppens insearchofstateandtraitemotionmarkersinmobilesensedlanguagefieldstudy |