A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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| 在线阅读: | http://hdl.handle.net/10361/22003 |
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10361-220032023-12-20T09:40:49Z A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users Nahar, Fatiha Binte Kamrun Afsana, Umme Halima Chowdhury, Azizul Muktadir Hasnaen, Maha Jahan, Sumaya Turzo, Esfar E Alam Department of Computer Science and Engineering, Brac University Data analytics Machine learning Natural language processing Random forest Suicide Detection of suicide Algorithms Bert Vader Text-preprocessing Depression Artificial neural network Natural language processing Machine learning. Artificial intelligence. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-40). In this research, we propose a hybrid model for predicting suicide risk from text data that incorporates BERT, VADER, and a Random Forest classifier for sentiment analysis. This model aims to identify individuals who may be at risk of committing suicide based on the tone of the text. The model is trained on a labelled dataset of text data that is either classified as ”suicide” or ”not suicide,” which provides the model with instances of text data that are linked with high or low suicide risk respectively. In order to extract feature representations of the text data, the BERT model is utilized, and the VADER model is utilized in order to extract sentiment ratings for each individual text. These features are integrated into a single feature vector for each text, and then the Random Forest classifier is trained using this feature vector. A number of different metrics, including accuracy, precision, recall, and F1-score, are utilized in order to assess the performance of the model. The findings of this research indicate that the hybrid model that was suggested is capable of accurately predicting the risk of suicide based on text data and that it is suitable for use as a tool to help clinical decision-making. The performance of the model to recognize patterns and trends in text data that are indicative of suicide risk holds promise for future research in the subject. Our novel composite model combining BERT, VADER with Random Forest Classifier has the accuracy of 82 percent. Fatiha Binte Kamrun Nahar Umme Halima Afsana Azizul Muktadir Chowdhury Maha Hasnaen Sumaya Jahan B.Sc. in Computer Science 2023-12-18T06:35:07Z 2023-12-18T06:35:07Z 2023 2023-01 Thesis ID: 19101500 ID: 19101427 ID: 22341040 ID: 19141002 ID: 22241182 http://hdl.handle.net/10361/22003 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. 40 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Data analytics Machine learning Natural language processing Random forest Suicide Detection of suicide Algorithms Bert Vader Text-preprocessing Depression Artificial neural network Natural language processing Machine learning. Artificial intelligence. |
| spellingShingle |
Data analytics Machine learning Natural language processing Random forest Suicide Detection of suicide Algorithms Bert Vader Text-preprocessing Depression Artificial neural network Natural language processing Machine learning. Artificial intelligence. Nahar, Fatiha Binte Kamrun Afsana, Umme Halima Chowdhury, Azizul Muktadir Hasnaen, Maha Jahan, Sumaya A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Turzo, Esfar E Alam |
| author_facet |
Turzo, Esfar E Alam Nahar, Fatiha Binte Kamrun Afsana, Umme Halima Chowdhury, Azizul Muktadir Hasnaen, Maha Jahan, Sumaya |
| format |
Thesis |
| author |
Nahar, Fatiha Binte Kamrun Afsana, Umme Halima Chowdhury, Azizul Muktadir Hasnaen, Maha Jahan, Sumaya |
| author_sort |
Nahar, Fatiha Binte Kamrun |
| title |
A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| title_short |
A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| title_full |
A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| title_fullStr |
A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| title_full_unstemmed |
A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users |
| title_sort |
machine learning-based approach for data analysis to ascertain suicidal individuals from social media users |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22003 |
| work_keys_str_mv |
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