TY - GEN
T1 - Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter
AU - Bandyopadhyay, Aritra
AU - Shenoy, K. Manjula
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Depression is a mental disorder that is characterized by a general mood or feeling of low self-esteem, loss of interest towards daily activities and low energy within a particular person. It is a very serious mental condition and its automatic detection through online social media platforms like Twitter could help identifying depressed individuals remotely. This paper suggests a novel method to extract tweets indicating depression using word lists. Various classification algorithms like SVM, KNN, Naive Bayes and Random Forests have been used to classify the individual tweets as to whether they indicate depression in the subject or not. Metrics like F1-Score has been used the verify and compare the results of the models using an unseen test dataset.
AB - Depression is a mental disorder that is characterized by a general mood or feeling of low self-esteem, loss of interest towards daily activities and low energy within a particular person. It is a very serious mental condition and its automatic detection through online social media platforms like Twitter could help identifying depressed individuals remotely. This paper suggests a novel method to extract tweets indicating depression using word lists. Various classification algorithms like SVM, KNN, Naive Bayes and Random Forests have been used to classify the individual tweets as to whether they indicate depression in the subject or not. Metrics like F1-Score has been used the verify and compare the results of the models using an unseen test dataset.
UR - https://www.scopus.com/pages/publications/85105946322
UR - https://www.scopus.com/inward/citedby.url?scp=85105946322&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73100-7_76
DO - 10.1007/978-3-030-73100-7_76
M3 - Conference contribution
AN - SCOPUS:85105946322
SN - 9783030730994
T3 - Advances in Intelligent Systems and Computing
SP - 1097
EP - 1106
BT - Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Future of Information and Communication Conference, FICC 2021
Y2 - 29 April 2021 through 30 April 2021
ER -
