Oriental Journal of Computer Science and Technology

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DEPRESSIKA: An Early Risk of Depression Detection through Opinions


Affiliations
1 Department of IT, LBEF (APUTI), Kathmandu, Nepal
 

Deep learning is a very dynamic area in Sentiment Classification. Text analytics is the process of understanding text and making actionable decisions and acting on it. be it Amazon Alexa, Siri, Cortana everything is made up of Natural Language Processing. Text to speech and Speech to text are generating so many data sets every day. The internet has the largest repository of data, it is hard to define what to exactly do with it. sentiment are the opinions or the way of feelings of the public usually in the sequential form, in which many people face difficulty in living their daily life. Some are even ending their life just they are depressed. The approach here is to help the people suffering from depression with appropriate methodology to use in this work. Depressika: Early Risk of Depression Detection with opinions is a web application which detects the early risk of depression from the social media posts created by the users with appropriate Recurrent Neural Networks [RNN]. This is a classification problem of the Machine Learning [ML]. Depressika builds on Waterfall Methodology of application development using the Keras, Tensor Flow, Scikit-Learn and Matplotlib to carryout and process sequential data and the overall process of development is carried out by Python programming Language.

Keywords

Depression Detection, Deep Learning [DL], Machine Learning [ML], Recurrent Neural Networks [RNN], Sequential, Sentiment Classification.
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  • Ranna Parekh, M.D., M.P.H., 2017 January. American Psychiatric Association, Depression: What is Depression?. [Online] Available at: https://www.psychiatry.org/patients-families/depression/what-is-depression [Accessed 12 May 2019].

  • Abdel-rahman Mohamed, G. E. D. a. G. H., 2006. Acoustic Modeling using Deep Belief Networks. Ieee Trans. On Audio, Speech, And Language Processing.

  • ADAA, 2018. Anxiety and Depression Association of America. [Online] Available at: https://adaa.org/understanding-anxiety/depression.

  • America, M. H., 2019. Online Depression Screening: Data and Statistics. [Online] Available at: http://www.mentalhealthamerica.net/online-depression-screening-data-and-statistics [Accessed 22 march 2019].

  • Bengio, Y., 2012. Deep Learning of Representations for Unsupervised and. JMLR: Workshop and Conference Proceedings, Workshop on Unsupervised and Transfer Learning, Issue 27, pp. 17-37.

  • booker, Cory, Jon Greenberg, 2019 March 21st. Politifact. [Online] Available at: https://www.politifact.com/truth-o-meter/statements/2019/mar/21/cory-booker/farmer-suicides-high-great-depression-no-data-says/ [Accessed 1 April 2019].

  • Browniee, J., 2016. Machine Learning Mastry. [Online] Available at: https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/8. Brownie, J., 2019-May-7. Machine Learning Mastry. [Online] Available at: https://machinelearning mastry.com/what-is-deep-learning [Accessed 11 5 2019].

  • Dishashree gupta, 2017. Analytics Vidhya: Fundamentals of Deep Learning – Introduction to Recurrent Neural Networks. [Online] Available at: https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/ [Accessed 02 05 2019].

  • English Oxford Living Dictionaries, 2019. Sentiment. [Online] Available at: https://en.oxforddictionaries.com/definition/sentiment [Accessed 14 may 2019].

  • Guru 99, 2018. guru 99: Database Design Tutorial: Learn Data Modelling. [Online] Available at: https://www.guru99.com/database-design.html [Accessed 25 September 2019].

  • Health, N. I. o. M., 2016. Depression basics. [Online] Available at: https://www.nimh.nih.gov/health/publications/depression/index.shtml#pub1

  • Hinton, G. a. S. R., 2006. Reducing the dimensionality of data with neural networks. science, 313(5786), pp. 504-507.

  • Jason Brownie, 2017 july 19. Machine Learning Mastry: What is deep Learning?. [Online] Available at: https://machinelearningmastery.com/what-is-deep-learning/ [Accessed 14 05 2019].


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  • DEPRESSIKA: An Early Risk of Depression Detection through Opinions

Abstract Views: 254  |  PDF Views: 1

Authors

Abhusan Chataut
Department of IT, LBEF (APUTI), Kathmandu, Nepal
Jyotir Moy Chatterjee
Department of IT, LBEF (APUTI), Kathmandu, Nepal
Rabi Shankar Rouniyar
Department of IT, LBEF (APUTI), Kathmandu, Nepal

Abstract


Deep learning is a very dynamic area in Sentiment Classification. Text analytics is the process of understanding text and making actionable decisions and acting on it. be it Amazon Alexa, Siri, Cortana everything is made up of Natural Language Processing. Text to speech and Speech to text are generating so many data sets every day. The internet has the largest repository of data, it is hard to define what to exactly do with it. sentiment are the opinions or the way of feelings of the public usually in the sequential form, in which many people face difficulty in living their daily life. Some are even ending their life just they are depressed. The approach here is to help the people suffering from depression with appropriate methodology to use in this work. Depressika: Early Risk of Depression Detection with opinions is a web application which detects the early risk of depression from the social media posts created by the users with appropriate Recurrent Neural Networks [RNN]. This is a classification problem of the Machine Learning [ML]. Depressika builds on Waterfall Methodology of application development using the Keras, Tensor Flow, Scikit-Learn and Matplotlib to carryout and process sequential data and the overall process of development is carried out by Python programming Language.

Keywords


Depression Detection, Deep Learning [DL], Machine Learning [ML], Recurrent Neural Networks [RNN], Sequential, Sentiment Classification.

References