Abstract
Depression is a mental illness of the human body that continuously affects human activities such as thinking capacity and physical appearance of the body. The emotional feeling of feeling low and dull toward the situation breaks down the career growth. Psychologists face a major problem in detecting depression at early stages. Patients find them difficult to interact with and share every thought regarding feeling they have. Major depressive disorder is characterized by sadness, worthlessness, disturbed slee** patterns and eating habits, and lethargy in activities that were once enjoyed. Social sites such as Facebook, Reddit, Twitter, Snapchat, etc., turn out a helpful way to express ideas and negative thoughts to feel free. Many researches have been completed on the dataset to detect depression. It turns out the part of the sentimental analysis by applying the machine algorithm such decision tree, random forest, naïve Bayes, Ensemble model, KNN, maximum entropy, etc. In this paper, the author studied various research to enhance and conclude the best algorithm and high accuracy, precision, and recall of depression detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al Asad N, Pranto MAM, Afreen S, Islam MM (2019, November). Depression detection by analyzing social media posts of user. In: 2019 IEEE international conference on signal processing, information, communication and systems (SPICSCON), pp 13–17. IEEE
Almouzini S, Alageel A (2019) Detecting Arabic depressed users from Twitter data. Procedia Comput Sci 163:257–265
AlSagri HS, Ykhlef M (2020) Machine learning-based approach for depression detection in Twitter using content and activity features. IEICE Trans Inf Syst 103(8):1825–1832
Arora P, Arora P (2019 March) Mining Twitter data for depression detection. In: 2019 international conference on signal processing and communication (ICSC), pp 186–189. IEEE
Chiong R, Budhi GS, Dhakal S (2021) Combining sentiment lexicons and content-based features for depression detection. IEEE Intell Syst 36(6)
Chiong R, Budhi GS, Dhakal S, Chiong F (2021) A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Comput Biol Med 104499
Fatima I, Abbasi BUD, Khan S, Al-Saeed M, Ahmad HF, Mumtaz R (2019) Prediction of postpartum depression using machine learning techniques from social media text. Expert Syst 36(4):e12409
Gaikar M, Chavan J, Indore K, Shedge R (2019, March) Depression detection and prevention system by analysing tweets. In: Proceedings 2019: conference on technologies for future cities (CTFC).
Govindasamy KA, Palanichamy N (2021, May) Depression detection using machine learning techniques on twitter data. In: 2021 5th international conference on intelligent computing and control systems (ICICCS), pp 960–966. IEEE
Narayanrao PV, Kumari PLS (2020, March) Analysis of machine learning algorithms for predicting depression. In: 2020 international conference on computer science, engineering and applications (ICCSEA), pp 1–4. IEEE
PY K, Dube R, Barbade S, Kulkarni G, Konda N, Konkati M (2021) Depression detection using machine learning. Available at SSRN 3851975
Skaik R, Inkpen D (2020, December) Using twitter social media for depression detection in the Canadian population. In: 2020 3rd artificial intelligence and cloud computing conference, pp 109–114
Sudha K, Sreemathi S, Nathiya B, RahiniPriya D Depression detection using machine learning
Tiwari G, Das G Machine learning based on approach for detection of depression using social media using sentiment analysis. Depression 9(10):16
Yalamanchili B, Kota NS, Abbaraju MS, Nadella VSS, Alluri SV (2020, February) Real-time acoustic based depression detection using machine learning techniques. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), pp 1–6. IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Varshney, T., Gupta, S., Goel, L. (2022). Literature Survey on Depression Detection Using Machine Learning. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2350-0_31
Download citation
DOI: https://doi.org/10.1007/978-981-19-2350-0_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2349-4
Online ISBN: 978-981-19-2350-0
eBook Packages: Computer ScienceComputer Science (R0)