Deep learning based depression detection from social media text

Research Article
Open access

Deep learning based depression detection from social media text

S. Anbukkarasi 1 , S. Jeevapriya 2 , A. Kaaviyaa 3 , T. Lawvanyapriya 4 , S. Monisha 5
  • 1 Department of Computer Science &Engineering, Kongu Engineering College, Perun-durai, Erode 638 001    
  • 2 Department of Computer Science &Engineering, Kongu Engineering College, Perun- durai, Erode 638 001.    
  • 3 Department of Computer Science &Engineering, Kongu Engineering College, Perun- durai, Erode 638 001.    
  • 4 Department of Computer Science &Engineering, Kongu Engineering College, Perun- durai, Erode 638 001.    
  • 5 Department of Computer Science &Engineering, Kongu Engineering College, Perun- durai, Erode 638 001.    
  • *corresponding author
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220632
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

Depression perception be a complex task on social media Complex properties associated with mental illness. There was a recent development in this area of research; social media platforms have established themselves as growing popularity. A basic part of people`s daily life. Social media platforms and their users share goals Relationships where the user's personal life is reflected on these platforms at several levels. Aside from the complexity associated with detecting mental illness through social media platforms, it is inherently difficult to obtain an enough annotated training data, so a supervised deep learning approach such as deep neural networks the implementation has not yet been widely implemented. We tried to find them for these reasons. The most effective deep learning model of the architectures selected in the previous architecture Achievements of supervised learning methods. The selected model will be used for recognition online users showing depression. Due to the limited amount of unstructured text data Extracted from social media text. Recently, Deep learning has been effectively used to a variety of application challenges, including stock market forecasting, traffic flow and accident risk forecasting, and mental disease diagnosis. Furthermore, deep learning has been used to predict sadness on social media and has outperformed classical machine learning method.

Keywords:

Deep Learning, social media, RNN, GRU., Bi- LSTM, LSTM, Depression Detection

Anbukkarasi,S.;Jeevapriya,S.;Kaaviyaa,A.;Lawvanyapriya,T.;Monisha,S. (2023). Deep learning based depression detection from social media text. Applied and Computational Engineering,2,657-663.
Export citation

References

[1]. Predicting Depression Severity using multimodal functions Cornell University AI and more, 2017.

[2]. P.V. Rajaraman, Asim Nath, Akshaya. P.R , Chatur Bhuja.G Depression Detection of Tweets and A Comparative Test, International Journal of Engineering Research & Technolo-gy(IJERT),Vol. 9 Issue 03, March-2020.

[3]. Liu, Bing, and Lei Zhang. ―A survey of opinion mining and sentiment analysis.‖ In Mining text data, pp. 415-463. Springer, Boston, MA, 2012.

[4]. Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumours from microblogs with recurrent neural networks. In: Ijcai, pp 3818–3824.

[5]. Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review Prajwal Kharel , Kalpana Sharma ,Sunil Dhimal , Sital Sharma Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019.

[6]. Depression detection from social network data using machine learning techniques Md Rafiqul Islam Ashad Kabir, Ashir Ahmed Springer, 2018

[7]. Pompili, M.; Innamorati, M.; Di Vittorio, C.; Sher, L.;Girardi,P.;Amore, M. Sociodemographic andclinical differences between suicide ideators and attempters: A study of mood disor-dered patients 50 years and older. Suicide Life-Threat. Behav. 2014, 44, 34– 45.

[8]. Centre for Behavioural Health Statistics and Quality. Results from the 2013 National Survey on Drug Use and Health: Mental Health Findings (HHS Publication No. SMA 14-4887, NSDUH Series H-49). Rockville, MD: Substance Abuse and Mental Health Services Ad-ministration; 2014.

[9]. Gehrmann, S.; Dernoncourt, F.; Li, Y.; Carlson, E.T.; Wu, J.T.; Welt, J.; Foote, J., Jr.; Mose-ley, E.T.; Grant, D.W.; Tyler, P.D.; et al. Comparing deep learning and concept extraction-based methods for patient phenotyping from clinical narratives. PLoS ONE 2018, 13, e0192360.

[10]. Orabi, A.H.; Buddhitha, P.; Orabi, M.H.; Inkpen, D. Deep learning for depression detection of twitter users. In Proceedings of the Fifth Workshop on Computational Linguistics and Clin-ical Psychology: From Keyboard to Clinic, New Orleans, LA, USA, 5 June 2018; pp. 88– 97.

[11]. Kim, J.; Lee, J.; Park, E.; Han, J. A deep learning model for detecting mental illness from user content on social media. Sci. Rep. 2020, 10, 11846

[12]. Sosa, P.M.; Sadigh, S. Twitter Sentiment Analysis with Neural Networks. 2016.

[13]. Cho, H.K. Twitter Depression Data Set Tweets Scraped from Twitter, Depressed and Non-Depressed. 2021.

[14]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.

[15]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand pre-diction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.

[16]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.

[17]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.

[18]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.

[19]. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.

[20]. Sathishkumar, V. E., Rahman, A. B. M., Park, J., Shin, C., & Cho, Y. (2020, April). Using machine learning algorithms for fruit disease classification. In Basic & clinical pharmacolo-gy & toxicology (Vol. 126, pp. 253-253). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[21]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.

[22]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

Cite this article

Anbukkarasi,S.;Jeevapriya,S.;Kaaviyaa,A.;Lawvanyapriya,T.;Monisha,S. (2023). Deep learning based depression detection from social media text. Applied and Computational Engineering,2,657-663.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Predicting Depression Severity using multimodal functions Cornell University AI and more, 2017.

[2]. P.V. Rajaraman, Asim Nath, Akshaya. P.R , Chatur Bhuja.G Depression Detection of Tweets and A Comparative Test, International Journal of Engineering Research & Technolo-gy(IJERT),Vol. 9 Issue 03, March-2020.

[3]. Liu, Bing, and Lei Zhang. ―A survey of opinion mining and sentiment analysis.‖ In Mining text data, pp. 415-463. Springer, Boston, MA, 2012.

[4]. Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumours from microblogs with recurrent neural networks. In: Ijcai, pp 3818–3824.

[5]. Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review Prajwal Kharel , Kalpana Sharma ,Sunil Dhimal , Sital Sharma Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019.

[6]. Depression detection from social network data using machine learning techniques Md Rafiqul Islam Ashad Kabir, Ashir Ahmed Springer, 2018

[7]. Pompili, M.; Innamorati, M.; Di Vittorio, C.; Sher, L.;Girardi,P.;Amore, M. Sociodemographic andclinical differences between suicide ideators and attempters: A study of mood disor-dered patients 50 years and older. Suicide Life-Threat. Behav. 2014, 44, 34– 45.

[8]. Centre for Behavioural Health Statistics and Quality. Results from the 2013 National Survey on Drug Use and Health: Mental Health Findings (HHS Publication No. SMA 14-4887, NSDUH Series H-49). Rockville, MD: Substance Abuse and Mental Health Services Ad-ministration; 2014.

[9]. Gehrmann, S.; Dernoncourt, F.; Li, Y.; Carlson, E.T.; Wu, J.T.; Welt, J.; Foote, J., Jr.; Mose-ley, E.T.; Grant, D.W.; Tyler, P.D.; et al. Comparing deep learning and concept extraction-based methods for patient phenotyping from clinical narratives. PLoS ONE 2018, 13, e0192360.

[10]. Orabi, A.H.; Buddhitha, P.; Orabi, M.H.; Inkpen, D. Deep learning for depression detection of twitter users. In Proceedings of the Fifth Workshop on Computational Linguistics and Clin-ical Psychology: From Keyboard to Clinic, New Orleans, LA, USA, 5 June 2018; pp. 88– 97.

[11]. Kim, J.; Lee, J.; Park, E.; Han, J. A deep learning model for detecting mental illness from user content on social media. Sci. Rep. 2020, 10, 11846

[12]. Sosa, P.M.; Sadigh, S. Twitter Sentiment Analysis with Neural Networks. 2016.

[13]. Cho, H.K. Twitter Depression Data Set Tweets Scraped from Twitter, Depressed and Non-Depressed. 2021.

[14]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.

[15]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand pre-diction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.

[16]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.

[17]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.

[18]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.

[19]. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.

[20]. Sathishkumar, V. E., Rahman, A. B. M., Park, J., Shin, C., & Cho, Y. (2020, April). Using machine learning algorithms for fruit disease classification. In Basic & clinical pharmacolo-gy & toxicology (Vol. 126, pp. 253-253). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[21]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.

[22]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.