ArticleStudies in health technology and informatics2022
Comparing Emotional Valence Scores of Twitter Posts from Manual Coding and Machine Learning Algorithms to Gain Insights to Refine Interventions for Family Caregivers of Persons with Dementia.
Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.Journal of nursing management · 2024Article
- Comparing Emotional Valence Scores of Twitter Messages from Human Coding and Machine Learning Algorithms Among Hispanic and African American Family Caregivers of Persons with Dementia.Studies in health technology and informatics · 2023Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
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5 authors.
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Abstract
We randomly extracted Korean-language Tweets mentioning dementia/Alzheimer's disease (n= 12,413) from November 28 to December 9, 2020. We independently applied three machine learning algorithms (Afinn, Syuzhet, and Bing) using natural language processing (NLP) techniques and qualitative manual scoring to assign emotional valence scores to Tweets. We then compared the means and distributions of the four emotional valence scores. Visual examination of the graphs produced indicated that each method exhibited unique patterns. The aggregated mean emotional valence scores from the NLP methods were mostly neutral, vs. slightly negative for manual coding (Afinn 0.029, 95% CI [-0.019, 0.077]; Syuzhet 0.266, [0.236, 0.295]; Bing -0.271, [-0.289, -0.252]; manual coding -1.601, [-1.632, -1.569]). One-way analysis of variance (ANOVA) showed no statistically significant differences among the four means after normalization. These findings suggest that the application of NLP can be fairly effective in extracting emotional valence scores from Korean-language Twitter content to gain insights regarding family caregiving for a person with dementia.
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Registered trials
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