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• W4239468669 abstract "<sec> <title>BACKGROUND</title> Happiness is considered as an important indicator of users’ mental and physical health. Fostering happiness has gained increasing public attention as one of the ways to decrease health costs in the long run. Understanding what makes users feel happy may help policy makers develop policies and methods that steer users towards behaviors identified to promote happiness. </sec> <sec> <title>OBJECTIVE</title> This paper aimed to investigate the use of deep learning methods to analyze happy moments and compare them with the traditional machine learning methods, which may provide a mechanism to accurately classify happy moments and help understand why users feel happy. </sec> <sec> <title>METHODS</title> A crowdsourced corpus of happy moments, HappyDB, was used. The dataset contained 14,125 posts with category labels that described sources and reasons for happy feelings: Achievement, Affection, Bonding, Enjoy the moment, Leisure, Nature and Exercise. We compared the performance of deep learning methods such as the convolutional neural network (CNN), bidirectional long-short term memory (Bi-LSTM), and attention Bi-LSTM with that of the traditional machine learning methods including logistic regression, SVM, and naïve Bayes. Standard measures including precision, recall, and F1 were adopted for each category. Macro-precision, macro-recall, and macro-F1 were used to evaluate the overall performance of the models. </sec> <sec> <title>RESULTS</title> We found that CNN achieved the best performance on macro-precision, macro-recall, and macro-F1, with values of 80.8, 79.3, and 80.0, respectively. Among the traditional machine learning methods, logistic regression performed the best, with macro-precision of 80.6, macro-recall of 71.1, and macro-F1 of 75.5. A detailed comparison of CNN and logistic regression on each category showed that CNN was able to improve F1 score for all categories. Specifically, F1 improved by at least 1.8% on the Bonding category and up to 11.3% on Nature. Performance improvements mainly depended on significant improvements on recall, especially for minor categories. For example, the recall of CNN was 80.9 and 70.9 for Nature and Exercise, which was an improvement of 28.5% and 11.6% compared with logistic regression. The reason was that CNN explicitly modeled the relationship between word features and the categories of happy moments by extracting important word features through convolution and pooling operations. </sec> <sec> <title>CONCLUSIONS</title> This is the first study to analyze happy moments based on deep learning methods. Compared with the traditional machine learning methods, deep learning methods, especially CNN, showed superiority on classifying the happy moments, which would facilitate understanding of the reasons why users feel happy and thus help policy makers formulate targeted policies to promote happiness. </sec>" @default.
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• W4239468669 date "2019-03-04" @default.
• W4239468669 modified "2023-09-27" @default.
• W4239468669 title "A Deep Learning Method to Analyze and Classify Happy Moments: A Comparative Analytic Study (Preprint)" @default.
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• W4239468669 doi "https://doi.org/10.2196/preprints.13894" @default.
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