Machine Classification for Suicide Ideation Detection on Twitter
Maidam Manisha1, Anuradha Kodali2, V. Srilakshmi3
1Maidam Manisha, PG Scholor, M.Tech, Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Affiliated to JNTUH, Hyderabad, India.
2Dr. Anuradha Kodali, Professor, Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Affiliated to JNTUH, Hyderabad, India.
3V.Srilakshmi, Assistant Professor, Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Affiliated to JNTUH, Hyderabad, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4154-4160 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36551081219/2019©BEIESP | DOI: 10.35940/ijitee.L3655.1081219
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© 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: The tremendous rise in technology and social media sites enabled everyone to express and share their thoughts and feelings with millions of people in the world. Online social networks like Google+, Instagram, Facebook, twitter, LinkedIn turned into significant medium for communication. With these sites, users can generate, send and receive data among large number of people. Along with the advantages, these platforms are having few issues about its user safety such as the build out and sharing suicidal thoughts. Therefore, in this paper we built a performance report of five Machine Learning algorithms called Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, and Prism, with the aim of identifying, classifying suicide related text on twitter and providing to the research related to the suicide ideation on communication networks. Firstly, these algorithms identify the most worrying tweets such as suicide ideation, reporting of suicidal thoughts, etc. Also, find outs the flippant to suicide. Along with ML classifiers, One of the most powerful NLP technologies i.e: Opinion summarization is used to classify suicidal and non-suicidal tweets. The outcome of the analysis representing that Prism classifier achieved good accuracy by observing emotions of people and extracting suicidal information from Twitter than other machine learning algorithms.
Keywords: DT, Machine Learning (ML), NB, Prism, RF, Suicide ideation, SVM, Social Networks , Text classification, World Health Organization.
Scope of the Article: Social Networks