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

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Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019) (ICCBI 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 49))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Software and Systems Engineering, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    M. Mounika & B. Valarmathi

  2. Department of Manufacturing, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    N. Srinivasa Gupta

Authors
  1. M. Mounika
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  2. N. Srinivasa Gupta
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  3. B. Valarmathi
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Corresponding author

Correspondence to B. Valarmathi .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Department of Business Administration, The Gerald Schwartz School of Business, StFX University, Antigonish, NS, Canada

    Ram Palanisamy

  3. Electrical and Computer Engineering, University of Applied Sciences, Egaleo, Attiki, Greece

    Klimis Ntalianis

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Mounika, M., Srinivasa Gupta, N., Valarmathi, B. (2020). Detection of Depression Related Posts in Tweets Using Classification Methods – A Comparative Analysis. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_70

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