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Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e. g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3, 168 features) suggesting there is no consistent count of features for predicting depressive-related tweets.
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression.
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