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dc.contributor.author
Ferrario, Andrea
dc.contributor.author
Demiray, Burcu
dc.contributor.author
Yordanova, Kristina
dc.contributor.author
Luo, Minxia
dc.contributor.author
Martin, Mike
dc.date.accessioned
2020-10-13T09:53:22Z
dc.date.available
2020-09-25T02:52:38Z
dc.date.available
2020-09-25T10:28:40Z
dc.date.available
2020-10-13T09:53:22Z
dc.date.issued
2020-09-15
dc.identifier.issn
1438-8871
dc.identifier.other
10.2196/19133
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/442475
dc.identifier.doi
10.3929/ethz-b-000442475
dc.description.abstract
©Andrea Ferrario, Burcu Demiray, Kristina Yordanova, Minxia Luo, Mike Martin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.09.2020. BACKGROUND: Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. OBJECTIVE: The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. METHODS: The methods in this study comprise (1) collecting and coding of transcripts of older adults' conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. RESULTS: Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. CONCLUSIONS: This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults' everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults' well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
JMIR
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Journal of Medical Internet Research
ethz.journal.volume
22
en_US
ethz.journal.issue
9
en_US
ethz.journal.abbreviated
J Med Internet Res
ethz.pages.start
e19133
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Toronto
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.date.deposited
2020-09-25T02:52:55Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-25T10:29:09Z
ethz.rosetta.lastUpdated
2021-02-15T18:05:10Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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