Detection of Depression Related Posts in Tweets Using Classification Methods – A Comparative Analysis

  • M. Mounika
  • N. Srinivasa Gupta
  • B. ValarmathiEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)


A 15-step pre-processing procedure is proposed to improve the accuracy of sentiment mining of depression related posts in the tweets. Perhaps, for the first time, converting emoticons in the depression related tweets into text form is proposed during the pre-processing stage. In this paper, Term Frequency-Inverse Document Frequency with n-grams is used for feature extraction. Sentiment analysis conducted on a dataset consisting of 1.6 million depression related tweets using the proposed pre-processing module with feature extraction using Term Frequency-Inverse Document Frequency with n-grams and Logistic Regression (LR) for classification resulted in 81% of accuracy in detecting depression related tweets.


Natural Language Processing Data mining Depression Twitter Logistic Regression MultiLayer Perceptron Bag of Words Term Frequency-Inverse Document Frequency 


  1. 1.
  2. 2.
    Tyshchenko, Y.: Depression and anxiety detection from blog posts data. Nature Precis. Sci. Inst. Comput. Sci. Univ. Tartu, Tartu Estonia (2018)Google Scholar
  3. 3.
    Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., Kaewpitakkun, Y.: Facebook social media for depression detection in the Thai community. In: 15th International Joint Conference on Computer Science and Software Engineering, Thailand (2018)Google Scholar
  4. 4.
    Oyong, I., Utami, E., Luthfi, E.T., White, K.: Natural language processing and lexical approach for depression symptoms screening of indonesian Twitter user. In: 10th International Conference on Information Technology and Electrical Engineering, China (2018)Google Scholar
  5. 5.
    Pirina, I., Coltekin, C.: Identifying depression on Reddit: the effect of training data. In: Proceedings of the 3rd Social Media Mining for Health Applications (SMM4H) Workshop & Shared Task, pp. 9–12 (2018)Google Scholar
  6. 6.
    Jamil, Z., Inkpen, D., Buddhitha, P.: Monitoring tweets for depression to detect at-risk users. In: Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology, pp. 32–40 (2017)Google Scholar
  7. 7.
    Yates, A., Cohan, A., Goharian, N.: Depression and self-harm risk assessment in online forums (2017)Google Scholar
  8. 8.
    Nadeem, M., Horn, M., Coppersmith, G., Sen, S.: Identifying depression on Twitter (2016)Google Scholar
  9. 9.
    Karmen, C., Hsiung, R.C., Wetter, T.: Screening internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods. J. Comput. Methods Programs Biomed. 120, 27–36 (2015)CrossRefGoogle Scholar
  10. 10.
    Wijaya, V., Erwin, A., Galinium, M., MuJiady, W.: Automatic mood classification of indonesian tweets using linguistic approach. IEEE J. (2013)Google Scholar
  11. 11.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  12. 12.
    Zhang, L., Hall, M., Bastola, D.: Utilizing Twitter data for analysis of chemoptherapy. Int. J. Med. Inform. 120, 92–100 (2018)CrossRefGoogle Scholar
  13. 13.
    Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of depression related posts in Reddit social media forum. IEEE J. (2019)Google Scholar
  14. 14.
    Hassan, A.U., Hussain, J., Hussain, M., Sadiq, M., Lee, S.: Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. IEEE J. (2017)Google Scholar
  15. 15.
    Subramani, S., Michalska, S., Wang, H., Du, J., Zhang, Y., Shakeel, H.: Deep learning for multi-class identification from domestic violence online posts. IEEE Access 7, 46210–46224 (2019)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Software and Systems Engineering, School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.Department of Manufacturing, School of Mechanical EngineeringVellore Institute of TechnologyVelloreIndia

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