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Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach
Accordingly, in order to collect appropriate data, we first need to construct a suitable lexicon and then use it to scrape for appropriate posts. The data are then manually labeled in preparation for training the ML algorithms.
In the process of lexicon construction, we carefully examined and reviewed major studies that delve into the correlation between social media and depression, aiming to compile essential terms for our depression lexicon [41,42].
JMIR AI 2023;2:e49531
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We applied lexicon-based sentiment analysis using 3 well-established sentiment dictionaries: BING, AFINN, and NRC. The BING dictionary was first designed around the domain of e-commerce customer reviews [12]; AFINN was created for synthesizing Twitter microblogs [13]; and NRC was a large, crowdsourced lexicon geared toward a more generalized domain [14]. We reported the number of unique words in each lexicon and the number of unique words labeled by each lexicon within our text data.
JMIR Med Educ 2023;9:e41953
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Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
Weibo-5 BML is a lexicon that contains 818 Chinese words or phrases that are annotated with 5 emotions (happiness, sadness, anger, fear, and disgust) [25]. The Weibo-5 BML lexicon has been used to identify mood changes of Weibo users, and the reliability of this lexicon has been verified [35].
J Med Internet Res 2023;25:e41823
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