Predicting suicidal intent from social media text post using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/17923 |
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10361-17923 |
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10361-179232023-03-22T05:37:06Z Predicting suicidal intent from social media text post using machine learning Chowdhury, Md. Mubin Ul Islam Hasan, Mehadi Nayem, A.K.M Muhibullah Meem, Humaira Tasnim Arif, Hossain Department of Computer Science and Engineering, Brac University Suicidal Ideation Machine Learning Deep Learning Neural Network Social media Functional Word Embedding Dense and GRU Bert Bi-directional LSTM. Suicide--Prevention. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). We are living in an age of modern science where cutting-edge technology has made the world so small. We can now easily connect with people worldwide via the internet and using social media. Social media has become a popular way to connect with people and to share our thoughts and feeling with the people we are connected. People are using social media as a tool where they share their feelings, daily life activities and so on. As a result, people are spending more times on those platforms to connect with people rather than in person. People who suffer from suicidal ideation are expressing their feelings and emotions on social platforms. As suicide is now an alarming problem in our society, we can use machine learning technology to determine suicidal ideation in the early stage based on social media data such as Twitter data and Reddit data. We have combined deep learning and an artificial neural network to make a model that we have named SIP (Suicidal Intent Prediction) which can detect suicidal ideation based on the text data of social media in the first place. In our proposed SIP model, we have used Functional, Word Embedding, Dense and GRU (Gated recurrent unit), Bi-directional LSTM, Bert to build our model. We have shown that our SIP model is able to determine the suicidal ideation with a higher training accuracy of 88%, a validation accuracy of 89% and training accuracy 98% and validation accuracy 99% from SIP (Sentiment) model. Md. Mubin Ul Islam Chowdhury Mehadi Hasan A.K.M Muhibullah Nayem Humaira Tasnim Meem B. Computer Science 2023-02-28T05:21:41Z 2023-02-28T05:21:41Z 2022 2022-05 Thesis ID: 18101640 ID: 18101657 ID: 18301117 ID: 17201103 http://hdl.handle.net/10361/17923 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 30 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Suicidal Ideation Machine Learning Deep Learning Neural Network Social media Functional Word Embedding Dense and GRU Bert Bi-directional LSTM. Suicide--Prevention. |
| spellingShingle |
Suicidal Ideation Machine Learning Deep Learning Neural Network Social media Functional Word Embedding Dense and GRU Bert Bi-directional LSTM. Suicide--Prevention. Chowdhury, Md. Mubin Ul Islam Hasan, Mehadi Nayem, A.K.M Muhibullah Meem, Humaira Tasnim Predicting suicidal intent from social media text post using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Chowdhury, Md. Mubin Ul Islam Hasan, Mehadi Nayem, A.K.M Muhibullah Meem, Humaira Tasnim |
| format |
Thesis |
| author |
Chowdhury, Md. Mubin Ul Islam Hasan, Mehadi Nayem, A.K.M Muhibullah Meem, Humaira Tasnim |
| author_sort |
Chowdhury, Md. Mubin Ul Islam |
| title |
Predicting suicidal intent from social media text post using machine learning |
| title_short |
Predicting suicidal intent from social media text post using machine learning |
| title_full |
Predicting suicidal intent from social media text post using machine learning |
| title_fullStr |
Predicting suicidal intent from social media text post using machine learning |
| title_full_unstemmed |
Predicting suicidal intent from social media text post using machine learning |
| title_sort |
predicting suicidal intent from social media text post using machine learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17923 |
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