Abstract
In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general population. Researchers have explored the use of machine learning algorithms to predict the likelihood of depression in individuals. As datasets related to depression become more abundant and machine learning technology advances, there is an opportunity to develop intelligent systems capable of identifying symptoms of depression in written material. By applying natural language processing and machine learning algorithms to analyze written text, such as social media posts, emails, and chat messages, researchers can potentially identify patterns and linguistic cues associated with depression. These patterns may include changes in word usage, tone, and sentiment. The dataset consists of text-based questions on this information channel. At present, machine learning techniques are highly effective for analyzing data and identifying problems. Researchers have conducted comparisons of the accuracy achieved by different machine learning algorithms using the complete set of attributes as well as a subset of selected attributes. In summary, while the potential for AI to aid in mental health diagnosis and treatment is exciting, it's important to proceed with care and consideration for the complexities of the field and the needs of patients.
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
References
Meera, S., Sonok, M., Adeethyia, S.: Predicting the utilization of mental health treatment with various machine learning algorithms. WSEAS Trans on Computers 19 (2019)
Usman, A., Jerry, C.L., Gautam, S.: Fuzzy explainable attention-based deep active learning on mental-health data. IEEE International Conference, pp. 6654–4407 (2019)
Konda, V., Nikhitha, K., Ashwath, R., Subba, R.N.V.: Predicting mental health illness using machine learning algorithm. Journal of Physics: Conference Series (2021)
Theodoros, I., Georgia, K., Mandani, N., Christina, L.: ILIOU Machine Learning Preprocessing Method for Depression Type Prediction, 257–263 (2019)
Kamal, M., et al.: Predicting Mental Illness using Social Media Posts and Comments 11 (2021). https://doi.org/10.14569/IJACSA.2020.0111271
Thanarajan, T., Alotaibi, Y., Rajendran, S., Nagappan, K.: Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition. AIMS Mathematics 8, 12520–12539 (2023)
Anishfathim, B., Sreenithi, B., Trisha, S., Swathi, J., Sindhu, P.M.: The Impact of Mental Health due to Covid 19 – A Mental Health Detector Using Machine Learning. Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 147 (2022)
Cho, H.K.: Twitter Depression Data Set Tweets Scraped from Twitter, Depressed and Non-Depressed (2021). Available online: https://www.kaggle.com/hyunkic/twitter-depression-dataset, accessed on 15 January 2022
Reya, P.R., Suchitra, S., Gopal, K.S.: The BMI and mental illness nexus: a machine learning approach. International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (2020)
Piyush, K., et al.: A machine learning implementation for mental health care. 11th International Conference on Cloud Computing, Data Science & Engineering (2021)
Soumya Raj, K., Anagha Raj, M., Amulya, N.: Level of stress and coping strategies among institutionalised and non-institutionalised elderly. Indian J. Public Health 11(03), 637 (2020)
Vaibhav, J., Dhruv, C., Piyush, G., Dinesh, K.V.: Depression and impaired mental health analysis from social media platforms using predictive modelling techniques. Fourth International Conference on I-SMAC (2020)
Amanat, A., et al.: Deep learning for depression detection from textual data. Electronics 11 (2022). https://doi.org/10.3390/electronics11050676
Surendran, R., Karthika, R., Jayalakshmi, B.: Implementation of dynamic scanner to protect the documents from ransomware using machine learning algorithms. In: 2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, United Kingdom, pp. 65–70. IEEE (2021)
Raymond, C., Gregorius, S.B., Sandeep, D., Fabian, C.: A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Computers in Biology and Medicine 135 (2021). https://doi.org/10.1016/j.compbiomed.2021.104499
Firoz, N., Beresteneva, O.G., Vladimirovich, A.S., Tahsin, M.S., Tafannum, F.: Automated Text-based Depression Detection using Hybrid ConvLSTM and Bi-LSTM Model. In: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 734–740. IEEE (2023)
Ramya, G.R., Bagavathi Sivakumar, P.: An incremental learning temporal influence model for identifying topical influencers on Twitter dataset. Soc. Netw. Anal. Min. 11, 27 (2021). https://doi.org/10.1007/s13278-021-00732-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tharun, S.V., Saranya, G., Tamilvizhi, T., Surendran, R. (2023). Highest Accuracy Based Automated Depression Prediction Using Natural Language Processing. In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-44084-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44083-0
Online ISBN: 978-3-031-44084-7
eBook Packages: Computer ScienceComputer Science (R0)