%0 Conference Proceedings
%T Data Set Creation and Empirical Analysis for Detecting Signs of Depression from Social Media Postings
%+ Sri Sivasubramaniya Nadar College of Engineering (SSN College of Engineering)
%A Kayalvizhi, S.
%A Durairaj, Thenmozhi
%< avec comité de lecture
%@ 978-3-031-16363-0
%( IFIP Advances in Information and Communication Technology
%B 5th International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Virtual, India
%Y Lekshmi Kalinathan
%Y Priyadharsini R.
%Y Madheswari Kanmani
%Y Manisha S.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-654
%P 136-151
%8 2022-03-24
%D 2022
%R 10.1007/978-3-031-16364-7_11
%K Depression
%K Data set
%K Data augmentation
%K Levels of depression
%K Random Forest
%Z Computer Science [cs]Conference papers
%X Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. There are many methods and modalities for detecting depression that involves physical examination of the individual. However, diagnosing mental health using their social media data is more effective as it avoids such physical examinations. Also, people express their emotions well in social media, it is desirable to diagnose their mental health using social media data. Though there are many existing systems that detects mental illness of a person by analysing their social media data, detecting the level of depression is also important for further treatment. Thus, in this research, we developed a gold standard data set that detects the levels of depression as ‘not depressed’, ‘moderately depressed’ and ‘severely depressed’ from the social media postings. Traditional learning algorithms were employed on this data set and an empirical analysis was presented in this paper. Data augmentation technique was applied to overcome the data imbalance. Among the several variations that are implemented, the model with Word2Vec vectorizer and Random Forest classifier on augmented data outperforms the other variations with a score of 0.877 for both accuracy and F1 measure.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-04381302/document
%2 https://inria.hal.science/hal-04381302/file/526570_1_En_11_Chapter.pdf
%L hal-04381302
%U https://inria.hal.science/hal-04381302
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-654