A Mixed Deep Learning Based Model to Early Detection of Depression
Keywords:Sentiment analysis, early risk detection, deep learning, mental health, depression identification, text classification
Mental health is considered as one of today’s world’s most prominent plagues. Therefore, our work aims to use the potential of social media platforms to solve one of mental health’s biggest issues, which is depression identification. We propose a new deep learning model that we train on a depression-dedicated dataset in order to detect such mental illness from an individual’s posts. Our main contributions lie in the three following points: (1) We trained our own word embeddings using a depression-dedicated dataset. (2) We combined a Convolutional Neural Networks model with the Message-level Sentiment Analysis model in order to improve the feature extraction process and enhance the model’s performance. (3) We analyzed through different experiments the performance of three deep learning models in order to provide more perspectives and insights for depression researches. A total of four classifier models were deployed with the same dataset. Those implementing CNN-BiLSTM with Attention model attained greater overall Accuracy, Recall, Precision and F1 macro scores of 0.97, 0.95, 0.84 and 0.92 on the final assessment test set, respectively.
Laura Andrade, Jorge J Caraveo-Anduaga, Patricia Berglund, Rob V Bijl, Ron De Graaf, Wilma Vollebergh, Eva Dragomirecka, Robert Kohn, Martin Keller, Ronald C Kessler, et al. The epidemiology of major depressive episodes: results from the international consortium of psychiatric epidemiology (icpe) surveys. International Journal of Methods in Psychiatric Research, 12(1):3–21, 2003.
Oscar Araque, Ignacio Corcuera-Platas, J Fernando Sanchez-Rada, and Carlos A Iglesias. Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77:236–246, 2017.
Christos Baziotis, Nikos Pelekis, and Christos Doulkeridis. Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 747–754, 2017.
Aaron T Beck, Calvin H Ward, Mock Mendelson, Jeremiah Mock, and John Erbaugh. An inventory for measuring depression. Archives of general psychiatry, 4(6):561–571, 1961.
Fidel Cacheda, Diego Fernández Iglesias, Francisco J. Nóvoa, and Victor Carneiro. Analysis and experiments on early detection of depression. In CLEF, 2018.
Xuetong Chen, Martin D. Sykora, Thomas W. Jackson, and Suzanne Elayan. What about mood swings: Identifying depression on twitter with temporal measures of emotions. In Companion Proceedings of the The Web Conference 2018, WWW ’18, pages 1653–1660. International World Wide Web Conferences Steering Committee, 2018.
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. Predicting depression via social media. In Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, July 8–11, 2013.
Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pages 160–167, New York, NY, USA, 2008. ACM.
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493–2537, 2011.
Glen Coppersmith, Mark Dredze, and Craig Harman. Quantifying mental health signals in twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 51–60. Association for Computational Linguistics, 2014.
Glen Coppersmith, Mark Dredze, Craig Harman, and Kristy Hollingshead. From adhd to sad: Analyzing the language of mental health on twitter through self-reported diagnoses. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 1–10. Association for Computational Linguistics, 2015.
Glen Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, and Margaret Mitchell. Clpsych 2015 shared task: Depression and ptsd on twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 31–39. Association for Computational Linguistics, 2015.
Munmun De Choudhury, Scott Counts, and Eric Horvitz. Social media as a measurement tool of depression in populations. In Proceedings of the 5th Annual ACM Web Science Conference, WebSci ’13, pages 47–56. ACM, 2013.
Thomas Fischer and Christopher Krauss. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2):654 – 669, 2018.
Yoon Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1746–1751, 2014.
Jitendra Kumar, Rimsha Goomer, and Ashutosh Kumar Singh. Long short term memory recurrent neural network (lstm-rnn) based workload forecasting model for cloud datacenters. Procedia Computer Science, 125:676–682, 2018. The 6th International Conference on Smart Computing and Communications.
Changliang Li, Bo Xu, Gaowei Wu, Saike He, Guanhua Tian, and Yujun Zhou. Parallel recursive deep model for sentiment analysis. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 15–26. Springer, 2015.
Wenwen Li and Michael Chau. Applying deep learning in depression detection. In 22nd Pacific Asia Conference on Information Systems, PACIS 2018, Yokohama, Japan, June 26–30, page 333, 2018.
David E. Losada and Fabio Crestani. A test collection for research on depression and language use. In Experimental IR Meets Multilinguality, Multimodality, and Interaction – 7th International Conference of the CLEF Association, CLEF 2016, Évora, Portugal, September 5–8, 2016, Proceedings, pages 28–39, 2016.
David E. Losada, Fabio Crestani, and Javier Parapar. erisk 2017: CLEF lab on early risk prediction on the internet: Experimental foundations. In Experimental IR Meets Multilinguality, Multimodality, and Interaction – 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings, pages 346–360, 2017.
David E. Losada, Fabio Crestani, and Javier Parapar. Overview of erisk: Early risk prediction on the internet. In Experimental IR Meets Multilinguality, Multimodality, and Interaction – 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10–14, 2018, Proceedings, pages 343–361, 2018.
Diego Maupomé and Marie-Jean Meurs. Using topic extraction on social media content for the early detection of depression. In CLEF: Conference and Labs of the Evaluation Forum, 2018.
Moin Nadeem. Identifying depression on twitter. arXiv preprint arXiv:1607.07384, 2016.
Sayanta Paul, Sree Kalyani Jandhyala, and Tanmay Basu. Early detection of signs of anorexia and depression over social media using effective machine learning frameworks. In CLEF: Conference and Labs of the Evaluation Forum, 2018.
Lenore Sawyer Radloff. The ces-d scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3):385–401, 1977.
Waleed Ragheb, Bilel Moulahi, Jérôme Azé, Sandra Bringay, and Maximilien Servajean. Temporal Mood Variation: at the CLEF eRisk-2018 Tasks for Early Risk Detection on The Internet. In CLEF: Conference and Labs of the Evaluation Forum, volume CEUR Workshop Proceedings, September 2018.
Stephen Robertson and Hugo Zaragoza. The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr., 3(4):333–389, April 2009.
Aliaksei Severyn and Alessandro Moschitti. Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 959–962. ACM, 2015.
Guangyao Shen, Jiang Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, and Wenwu Zhu. Depression detection via harvesting social media: A multimodal dictionary learning solution. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI’17, pages 3838–3844. AAAI Press, 2017.
Amit Singhal. Modern information retrieval: a brief overview. Bulletin of IEEE Computer Society Technical Committee on Data Engineering, 24:2001, 2001.
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631–1642, 2013.
Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin, and Ivan Smirnov. Feature engineering for depression detection in social media. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods – Volume 1: ICPRAM,, pages 426–431. INSTICC, SciTePress, 2018.
Zengcai Su, Hua Xu, Dongwen Zhang, and Yunfeng Xu. Chinese sentiment classification using a neural network tool word2vec. In Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on, pages 1–6. IEEE, 2014.
Yoshihiko Suhara, Yinzhan Xu, and Alex ‘Sandy’ Pentland. Deep-mood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In Proceedings of the 26th International Conference on World Wide Web, WWW ’17, pages 715–724. International World Wide Web Conferences Steering Committee, 2017.
Duyu Tang, Furu Wei, Bing Qin, Ting Liu, and Ming Zhou. Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 208–212, 2014.
Marcel Trotzek, Sven Koitka, and Christoph M Friedrich. Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. arXiv preprint arXiv:1804.07000, 2018.
Marcel Trotzek, Sven Koitka, and Christoph M. Friedrich. Word embeddings and linguistic metadata at the clef 2018 tasks for early detection of depression and anorexia. In CLEF: Conference and Labs of the Evaluation Forum, 2018.
Duy-Tin Vo and Yue Zhang. Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pages 1347–1353. AAAI Press, 2015.
Yu-Tseng Wang, Hen-Hsen Huang, and Hsin-Hsi Chen. A neural network approach to early risk detection of depression and anorexia on social media text. In CLEF: Conference and Labs of the Evaluation Forum, 2018.
You Zhang, Jin Wang, and Xuejie Zhang. Ynu-hpcc at semeval-2018 task 1: Bilstm with attention based sentiment analysis for affect in tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 273–278. Association for Computational Linguistics, 2018.
Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 207–212. Association for Computational Linguistics, 2016.
Shusen Zhou, Qingcai Chen, and Xiaolong Wang. Active deep learning method for semi-supervised sentiment classification. Neurocomputing, 120:536–546, 2013.
William WK Zung, Carolyn B Richards, and Marvin J Short. Self-rating depression scale in an outpatient clinic: further validation of the sds. Archives of General Psychiatry, 13(6):508–515, 1965.