Forensic Suicidal Inquiry of Depressed Individuals using LSTM and Convolutional Neural Networks
Chandrika Prasad1, Anurag Hakeem2, Rishabh Malhotra3, Shahroz Ahmad Jan4, Urvash Pratap Singh5, Jagdish S. Kallimani6
1Chandrika Prasad, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
2Anurag Hakeem, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
3Rishabh Malhotra, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
4Shahroz Ahmad Jan, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
5Urvash Pratap Singh, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
6Dr Jagdish S. Kallimani, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 105-110 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10200486S419/19©BEIESP | DOI: 10.35940/ijitee.F1020.0486S419
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media sites such as Twitter, Facebook, Tum blretc, are vastly popular among the general population. People post updates, tweets etc., and almost 75% of the times, these posts are a combination of emotions. The idea is to analyze suicidal-depression tendencies in adults with traumatizing experiences or socio-economic difficulties. This makes the overall analysis of sentiments especially extremely complex, which we aim to resolve here in this project by breaking down all the sentences into individual words, and along with emoticons and hashtags, converting each one of them into tokens, and then applying deep learning algorithms on the same, to accurately determine the sentiments of given messages. The objective of the project undertaken is to determine the suicidal- sentiment of various depressed individuals, and how likely is it that they are inclined to commit suicide on the basis of their tweets.
Keywords: This Makes the Overall Analysis of Sentiments Especially Extremely Complex,
Scope of the Article: Computer Science and Its Applications