TY - JOUR
T1 - Characterizing negative sentiments in at-risk populations via crowd computing
T2 - a computational social science approach
AU - Garcia-Mancilla, Jesus
AU - Ramirez-Marquez, Jose E.
AU - Lipizzi, Carlo
AU - Vesonder, Gregg T.
AU - Gonzalez, Victor M.
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Drawing on psychological theory, we created a new approach to classify negative sentiment tweets and presented a subset of unclassified tweets to humans for categorization. With these results, a tweet classification distribution was built to visualize how the tweets can fit in different categories. The approach developed through visualization and classification of data could be an important base to measure the efficiency of a machine classifier with psychological diagnostic criteria as the base (Thelwall et al. in J Assoc Inf Sci Technol 62(4):406–418, 2011). Nonetheless, this proposed system is used to identify red flags in at-risk population for further intervention, due to the need to be validated through therapy with an expert.
AB - Drawing on psychological theory, we created a new approach to classify negative sentiment tweets and presented a subset of unclassified tweets to humans for categorization. With these results, a tweet classification distribution was built to visualize how the tweets can fit in different categories. The approach developed through visualization and classification of data could be an important base to measure the efficiency of a machine classifier with psychological diagnostic criteria as the base (Thelwall et al. in J Assoc Inf Sci Technol 62(4):406–418, 2011). Nonetheless, this proposed system is used to identify red flags in at-risk population for further intervention, due to the need to be validated through therapy with an expert.
KW - Crowd computing
KW - Depression characterization
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85088165791&partnerID=8YFLogxK
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U2 - 10.1007/s41060-018-0135-9
DO - 10.1007/s41060-018-0135-9
M3 - Article
AN - SCOPUS:85088165791
SN - 2364-415X
VL - 7
SP - 165
EP - 177
JO - International Journal of Data Science and Analytics
JF - International Journal of Data Science and Analytics
IS - 3
ER -