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A Sentiment Analysis of Suicidal Notes using Machine Learning

by Farziz Aktar Ahmed, Junumoni Khakhlari, Nitumani Sarmah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 22
Year of Publication: 2022
Authors: Farziz Aktar Ahmed, Junumoni Khakhlari, Nitumani Sarmah
10.5120/ijca2022922254

Farziz Aktar Ahmed, Junumoni Khakhlari, Nitumani Sarmah . A Sentiment Analysis of Suicidal Notes using Machine Learning. International Journal of Computer Applications. 184, 22 ( Jul 2022), 9-15. DOI=10.5120/ijca2022922254

@article{ 10.5120/ijca2022922254,
author = { Farziz Aktar Ahmed, Junumoni Khakhlari, Nitumani Sarmah },
title = { A Sentiment Analysis of Suicidal Notes using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2022 },
volume = { 184 },
number = { 22 },
month = { Jul },
year = { 2022 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number22/32447-2022922254/ },
doi = { 10.5120/ijca2022922254 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:07.439603+05:30
%A Farziz Aktar Ahmed
%A Junumoni Khakhlari
%A Nitumani Sarmah
%T A Sentiment Analysis of Suicidal Notes using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 22
%P 9-15
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Suicide becomes an unavoidable issues for the modern society. Every year 800000 peoples loses their lives worldwide. Detecting sentiments of these peoples is a new challenge for many researchers. It can be achieved by analyzing their statements made by the victims before they commit any suicidal actions, with the help of machine learning, natural language processing etc. In this paper, we have used a dataset which contains 232074 unique values collected posts from “Suicide Watch” and “depression” subreddits of the Reddit platform, to develop different machine learning model to analyze the sentiments of these data. We developed several types of machine learning model to compare the accuracy and find out the best and suitable algorithm for the project of detecting people's sentiment. The accuracy we able to achieved, SVM 57.24%, Naive Bayes (Gaussian) 54.69%, Random forest67.67%, Decision tree 70.95. Along with these algorithms we have also developed different versions of Naïve Bayes model algorithm where Naïve Bayes (Bernoulli), Naïve Bayes (Multinomial) and Naïve Bayes (Gaussian) able to achieve an accuracy of 49.92%, 51.65%, 54.69% accordingly. Here we have found that Decision Tree is providing best accuracy compare to another model algorithm. In addition, among all the versions of Naive Bayes model algorithms Bayes (Gaussian) is providing the best accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Suicide attempts Decision Tree algorithm Machine Learning Support Vector Machine Random Forest Naive Bayes Deep Learning.

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