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A Depth Study on Suicidal Thoughts in the Online Social Networks

  • S. KavipriyaEmail author
  • A. Grace Selvarani
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Online Social Network acts as platforms for users to communicate with one another and to share their feeling online. Few category of social media users utilizes the platform towards positing aggressive data. The automatic identification of the aggressive data can be identified by employing data mining algorithm utilizing machine learning principles. The standard machine learning approaches works with training, validation, and testing phases, and considered features such as part-of-speech, frequencies of insults and sentiment has been considered for emotions traits collected from the facebook data which leads several challenges to the system performance. In order to tackle particular issues, various technique employed in the literatures has been discussed in depth. In this paper, we undergo a detailed survey on technique employed to detect the suicide oriented traits on integration of sentiment analysis, Negative matrix factorization and summed up direct relapse calculation to analyze the connection between enthusiastic qualities and suicide chance and synthetic minority over- sampling technique is used in order to extract the information from a large collection of dataset. The ID3, C4.5, Apriori algorithm, association rule mining and naïve Bayes models has been used to predict who have suicidal ideation to repeatedly commit suicide attempts. Those techniques incorporate the linguistic features to regulate the durability of the quality on the count of self destruction. The issue attain were unique and remain to have a powerful segment with the count of self destruction. On this study, more meaningful insight about self destruction has been gathered.

Keywords

Sentiment analysis Data mining Emotion traits analysis Online social networks Opinion mining 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sri Ramakrishna Engineering CollegeCoimbatoreIndia

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