DEPRESSION DETECTION FROM SOCIAL MEDIA DATA USING CNN AND LINGUISTIC METADATA FEATURES

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SNEHA Pullaboina
J.Rachana
Resu sai chand goud
SATHYAM LAXMI VENKATA PRASAD

Abstract

: This project mainly concentrates on the comparison of various machine learning algorithms in predicting depression at an early stage. We take a popular reddit dataset as an input and train various models to find the best one among them. Programming language used is python and hybrid algorithms are helpful for detecting the most accurate algorithm that can be used to predict depression using various linguistic metadata features. Depression is a prevalent mental illness characterized by a depressed mood, stressful life experiences, and a sense of despair. It has an impact on your mood and capacity to operate, and it has the potential to lead to suicide. Depression is a substantial contributor to the worldwide burden of mental diseases and is a main cause of disability. According to studies, women are more likely than males to suffer from depression. Around 700,000 people commit suicide each year. Suicide is the fourth leading cause of mortality among people between the ages of 15 and 29. Depression is a common illness that affects 3.8 % of the world's population, with 5.0 % and 5.7 % of people over the age of 60 suffering from it. We target the early diagnosis of sadness in this study by applying several Machine Learning algorithms based on messages and posts on social media networks. Based on word embedding methods WORD2VEC, GLOVE, the Machine Learning algorithm LOGISTIC REGRESSION and the neural network algorithm CONVOLUTIONAL NEURAL NETWORK(CNN) are trained and compared to a classification-based user-level Linguistic metadata

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How to Cite
Pullaboina, S., J.Rachana, Resu sai chand goud, and SATHYAM LAXMI VENKATA PRASAD. “DEPRESSION DETECTION FROM SOCIAL MEDIA DATA USING CNN AND LINGUISTIC METADATA FEATURES”. Technix International Journal for Engineering Research, vol. 9, no. 7, July 2022, pp. 30-35, https://tijer.org/index.php/tijer/article/view/224.
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Research Articles

References

. Marcel Trotsky, Sven Ketika, and Christoph M. Friedrich, “Utilizing Neural Networks and Linguistic

Metadata for Early Detection of Depression Indications in Text Sequences” vol.32,2020 pp 3-14.

. J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” in Proc. 56th

Anu. Meeting Assoc. Computer. Linguistics (Vol. 1: Long Papers), 2018, vol. 1, pp. 328–339.

. M. Al-Mousawi and T. Johnstone, “In an absolute state: Elevated use of absolutist words is a marker

specific to anxiety, depression, and suicidal ideation,” Clinical Psychological Sci., vol. 6, no. 4, pp. 529–542,

. P. Bojanowski, E. Grave, A. Joplin, and T. Manolov, “Enriching word vectors with subworld information,”

Trans. Assoc. Computer. Linguistics, vol. 5, pp. 135–146, 2017.

. Y. Zhang and B. Wallace, “A sensitivity analysis of convolutional neural networks for sentence

classification,” in Proc. 8th Int. Joint Conf. Natural Lang. Process. (Vol. 1: Long Papers), 2017, pp. 253–26

. H. Almeida, A. Briand, and M.-J. Mears, “Detecting early risk of depression from social media user-

generated content,” in Proc. Conf. Labs Eval. Forum, 2017. [Online]. Available: http://ceur-ws. org/Vol-

/paper_127.pdf

. E. S. Paykel, “Basic concepts of depression,” Dialogues Clinical Neuroscience, vol. 10, no. 3, pp. 279–

, 2017.

. P. S. Wang, M. Anger Meyer, G. Borges. Chihuly. Huang, et al., “Delay and failure in treatment seeking

after first onset of mental disorders in the WHO,” vol. 6, no. 3, 2016, Art. no. 177.

. W. Shang, K. Sohn, D. Almeida, and H. Lee, “Understanding and improving convolutional neural

networks via concatenated rectified linear units,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, vol. 48, pp.

–2225.

. R. Whitley and R. D. Campbell, “Stigma, agency and recovery amongst people with severe mental

illness,” Social Sci. Med., vol. 107, pp. 1–8, 2015

. T. Manolov, I. Subsieve, K. Chen, G. S. Corrido, and J. Dean, “Distributed representations of words

and phrases and their compositionality,” in Proc. Neural Inf. Process. Syst., 2014, pp. 3111–3119.

. J. A. Nayland, S. W. Grande, K. A. Asch Brenner, and G. Elwyn, “Naturally occurring peer support

through social media: the experiences of individuals with severe mental illness using YouTube,” PLOS One,

vol. 9, no. 10, 2014, Art. no. e110171.

. A. Nayland, K. A. Asch Brenner, L. A. Marsh, and S. J. Bartels, “The future of mental health care:

Peer-to-peer support and social media,” Epidemiology Psychiatric Sci., vol. 25, no. 2, pp. 113–122, 2014.

. M. Berger, T. H. Wagner, and L. C. Baker, “Internet use and stigmatized illness,” Social Sci. Med., vol.

, no. 8, pp. 1821–1827, 2014. 59

. W. Bucci and N. Freedman, “The language of depression,” Bulletin Menninger Clinic, vol. 45, no. 4,

pp. 334–358, 2013