Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach
DOI:
https://doi.org/10.38032/jea.2021.01.006Keywords:
Depression, CNN-LSTM, SVM, Word Embedding, Neural NetworkAbstract
At present, depression is the main reason for suicidal death. Depression also causes different kinds of diseases. Nowadays, people are deeply involved in social media and like to share their feelings on social media. So, it becomes easy to analyze depression through social media. In this paper, a combination of two CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models has been proposed to make a hybrid CNN-LSTM model, CNN has performed for the image to create a matrix, and LSTM has given the result from the given matrix. In this paper, datasets are prepared based on depression and non-depression-related status. The proposed method has been applied to that dataset. The best result has been obtained using a hybrid neural network with the word embedding technique using the Bengali Facebook status dataset. We have used the SVM (Support Vector Machine) model to predict a small dataset of Bengali Facebook status and count vectorizer to count the word in the document. Finally, this paper has built up a model that makes strength and support for deep learning architecture.
References
"What Is Depression?" https://www.psychiatry.org/patients-families/depression/what-is-depression (accessed Jun. 06, 2020).
Choudhury, A.A., Khan, M.R.H., Nahim, N.Z., Tulon, S.R., Islam, S. and Chakrabarty, A., 2019, June. Predicting depression in Bangladeshi undergraduates using machine learning. In 2019 IEEE Region 10 Symposium (TENSYMP) (pp. 789-794). IEEE. DOI: 10.1109/TENSYMP46218.2019.8971369. DOI: https://doi.org/10.1109/TENSYMP46218.2019.8971369
"Can Depression Really Kill You?" https://www.verywellmind.com/can-depression-kill-you-1067514 (accessed Jun. 06, 2020).
Islam, M.R., Kabir, M.A., Ahmed, A., Kamal, A.R.M., Wang, H. and Ulhaq, A., 2018. Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6(1), pp.1-12. DOI: 10.1007/s13755-018-0046-0. DOI: https://doi.org/10.1007/s13755-018-0046-0
"Depression (major depressive disorder) - Symptoms and causes - Mayo Clinic." https://www.mayoclinic.org/diseases-conditions/depression/symptoms-causes/syc-20356007 (accessed Jun. 06, 2020).
"What is mental illness? - MindFreedom International (MFI)." https://mindfreedom.org/kb/voices-for-choices/voices-for-choices-what-is-mental-illness/ (accessed Jun. 06, 2020).
"Depression: What it is, symptoms, causes, treatment, types, and more." https://www.medicalnewstoday.com/articles/8933 (accessed Jun. 06, 2020).
"World Health Organization, Depression: Let's talk." http://www.searo.who.int/bangladesh/enbanwhd2017/en/ (accessed Jun. 06, 2020).
Selim, N., 2010. Cultural dimensions of depression in Bangladesh: a qualitative study in two villages of Matlab. Journal of Health, Population, and Nutrition, 28(1), p.95. DOI: https://doi.org/10.3329/jhpn.v28i1.4528
Anderson, J.E., Michalak, E.E. and Lam, R.W., 2002. Depression in primary care: Tools for screening, diagnosis, and measuring response to treatment. British Columbia Medical Journal, 44(8), pp.415-419.
Billah, M. and Hassan, E., Depression Detection from Bangla Facebook Status using Machine Learning Approach. International Journal of Computer Applications, 975, p.8887. DOI: 10.5120/ijca2019919314. DOI: https://doi.org/10.5120/ijca2019919314
Ramalingam, D., Sharma, V. and Zar, P., 2019. Study of depression analysis using machine learning techniques. Int. J. Innov. Technol. Explor. Eng, 8(7C2), pp.187-191.
Uddin, A.H., Bapery, D. and Arif, A.S.M., 2019, July. Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1-4). IEEE. DOI: 10.1109/IC4ME247184.2019.9036528. DOI: https://doi.org/10.1109/IC4ME247184.2019.9036528
"NLTK Book." https://www.nltk.org/book/ (accessed Jun. 05, 2020).
"Understanding of Convolutional Neural Network (CNN) — Deep Learning." https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 (accessed Jun. 14, 2020).
"An intuitive guide to Convolutional Neural Networks." https://www.freecodecamp.org/news/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050/ (accessed Jun. 14, 2020).
"Long Short Term Memory | Architecture Of LSTM. " https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/ (accessed Jun. 14, 2020).
Zhou, C., Sun, C., Liu, Z. and Lau, F., 2015. A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630. Available: http://arxiv.org/abs/1511.08630.
Rakib, O.F., Akter, S., Khan, M.A., Das, A.K. and Habibullah, K.M., 2019, December. Bangla word prediction and sentence completion using GRU: an extended version of RNN on N-gram language model. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/STI47673.2019.9068063
Emon, E.A., Rahman, S., Banarjee, J., Das, A.K. and Mittra, T., 2019, June. A deep learning approach to detect abusive bengali text. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICSCC.2019.8843606
Hossain, M.M., Labib, M.F., Rifat, A.S., Das, A.K. and Mukta, M., 2019, June. Auto-correction of English to Bengali Transliteration System using Levenshtein Distance. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICSCC.2019.8843613
Drovo, M.D., Chowdhury, M., Uday, S.I. and Das, A.K., 2019, June. Named Entity Recognition in Bengali Text Using Merged Hidden Markov Model and Rule Base Approach. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICSCC.2019.8843661
Biswas, E. and Das, A.K., 2019, June. Symptom-Based Disease Detection System In Bengali Using Convolution Neural Network. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICSCC.2019.8843664
Das, A.K., Ashrafi, A. and Ahmmad, M., 2019, February. Joint Cognition of Both Human and Machine for Predicting Criminal Punishment in Judicial System. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 36-40). IEEE. DOI: https://doi.org/10.1109/CCOMS.2019.8821655
Tuhin, R.A., Paul, B.K., Nawrine, F., Akter, M. and Das, A.K., 2019, February. An automated system of sentiment analysis from Bangla text using supervised learning techniques. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 360-364). IEEE. DOI: https://doi.org/10.1109/CCOMS.2019.8821658
Islam, J., Mubassira, M., Islam, M.R. and Das, A.K., 2019, February. A speech recognition system for Bengali language using recurrent Neural network. In 2019 IEEE 4th international conference on computer and communication systems (ICCCS) (pp. 73-76). IEEE. DOI: https://doi.org/10.1109/CCOMS.2019.8821629
Bhuiyan, M., Rahman, A., Ullah, M. and Das, A.K., 2019. iHealthcare: Predictive model analysis concerning big data applications for interactive healthcare systems. Applied Sciences, 9(16), p.3365. DOI: https://doi.org/10.3390/app9163365
Labib, M.F., Rifat, A.S., Hossain, M.M., Das, A.K. and Nawrine, F., 2019, June. Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICSCC.2019.8843640
Tadesse, M.M., Lin, H., Xu, B. and Yang, L., 2020. Detection of suicide ideation in social media forums using deep learning. Algorithms, 13(1), p.7. DOI: 10.3390/a13010007. DOI: https://doi.org/10.3390/a13010007
Downloads
Published
- Abstract view1499