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

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

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.

Keywords

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

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Copyright information

© 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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