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)


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.


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


  1. 1.
    Statista: Most popular reasons for internet users worldwide to use social media as of 3rd quarter 2017.
  2. 2.
    Kamps, J., Marx, M., Mokken, R.J., De Rijke, M.: Using WordNet to measure semantic orientations of adjectives. In: Proceedings of International Conference Language Resource Evaluation, pp. 1115–1118 (2004)Google Scholar
  3. 3.
    Hu, M., Liu, B.: Opinion feature extraction using class sequential rules. Presented at the AAAI Spring Symposium Computational Approaches Analyzing Weblogs, Palo Alto, CA, USA. Paper AAAI-CAAW 2006 (2006)Google Scholar
  4. 4.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. J. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  5. 5.
    Google Cloud: GoogleCloudTranslationAPIDocumentation.
  6. 6.
    Steven Bird, E.L., Klein, E.: Natural Language Processing with Python. O’Reilly Media (2009)Google Scholar
  7. 7.
    Akaichi, J., Dhouioui, Z., Lopez-HuertasPerez, M.J.: Text mining Facebook status updates for sentiment classification. In: System Theory, Control and Computing (ICSTCC), 2013 17th International Conference, Sinaia, pp. 640–645 (2013)Google Scholar
  8. 8.
    Ku, L.-W., Liang, Y.-T., Chen, H.-H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI SpringSymposium, Computational Approaches Analyzing Weblogs, pp. 100–107 (2006)Google Scholar
  9. 9.
    Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L.: A depression detection model based on sentiment analysis in micro-blog social network. In: PAKDD Workshop, pp. 201–213 (2013)Google Scholar
  10. 10.
    Golbeck, J.A.: Computing and applying trust in web-based social networks. Ph.D. dissertation, Graduate School of the University of Maryland, CollegePark (2005)Google Scholar
  11. 11.
    Tai, Y.M., Chiu, H.W.: Artificial neural network analysis on suicide and self-harm history of Taiwanese soldiers. In: Second International Conference on Innovative Computing, Information and Control (ICICIC 2007), p. 363, Kumamoto, Japan. IEEE (2007)Google Scholar
  12. 12.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Google eBook (2011)CrossRefGoogle Scholar
  13. 13.
    Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)Google Scholar
  14. 14.
    De Choudhury, M., Gamon, M.: Predicting depression via social media. In: Proceedings of Seventh International AAAI Conference on Weblogs Social Media, vol. 2, pp. 128–137 (2013)Google Scholar
  15. 15.
    Ramirez-Esparza, N., Chung, C.K., Kacewicz, E., Pennebaker, J.W.: The psychology of word use in depression forums in English and in Spanish: testing two text analytic approaches. Association for the Advancement of Artificial Intelligence (
  16. 16.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon- based methods for sentiment analysis. J. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sri Ramakrishna Engineering CollegeCoimbatoreIndia

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