Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.

Bibliographic Details
Main Authors:	Rodela, Raisa Rahman, Efty, Farhan Tanvir, Rahman, Mubashira, Wajiha, Shaira
Other Authors:	Reza, Md Tanzim
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	Schizophrenia Logistic regression Mental illness Decision tree Language pattern Social media post Natural language processing GRU Bi-LSTM BERT Natural language processing (Computer science) Machine learning
Online Access:	http://hdl.handle.net/10361/22875

id	10361-22875
record_format	dspace
spelling	10361-228752024-05-20T09:11:21Z Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP Rodela, Raisa Rahman Efty, Farhan Tanvir Rahman, Mubashira Wajiha, Shaira Reza, Md Tanzim Rahman, Rafeed Department of Computer Science and Engineering, Brac University Schizophrenia Logistic regression Mental illness Decision tree Language pattern Social media post Natural language processing GRU Bi-LSTM BERT Natural language processing (Computer science) Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 80-81). Schizophrenia is one of the destructive personality disorders where people have unusual interpretations of reality and are lured to develop harmful actions if not diagnosed promptly. This study focuses on identifying language patterns indicative of schizophrenic-prone texts in online communication and intends to contribute to the development of early intervention techniques in mental health utilizing ML and NLP methods. This study used two datasets to examine language patterns associated with schizophrenia in social media posts. The first dataset, Pre existing obtained from a repository focused on identifying schizophrenia-related postings, functions as a standard for comparison and evaluation. The second dataset, New scrapped obtained by extracting information from subreddits associated with schizophrenia, offers a more extensive range of language patterns. The dual-phase technique entails training models using the existing dataset and evaluating their performance on the newly collected dataset. The research uses various models, including transformer model BERT, recurrent neural network model Bi-LSTM, and GRU, as well as machine learning models such as Support Vector Classifier, Logistic Regression, Multinomial Naive Bayes, Random Forest, and Decision Tree to predict whether textual data is suggestive of schizophrenia. The language patterns of schizophrenic-prone texts differ from texts written by mentally-healthy individuals, encompassing phonological, morphological, and syntactic aspects. These models can analyze linguistic patterns and acquire knowledge about them. The results achieved after the training of the models are outstanding. The DistilBERT transformer model achieves 97% and 84% accuracy, GRU achieves high accuracy rates of 91% and 79%, the logistic regression machine learning model demonstrates impressive efficiency with accuracy rates of 93% and 83% respectively for Pre existing and New scrapped dataset. In order to ensure the models can effectively handle new data, we conducted a contemporary comparison. This analysis revealed that consistent data collection is necessary for accurate predictive results. Raisa Rahman Rodela Farhan Tanvir Efty Mubashira Rahman Shaira Wajiha B.Sc in Computer Science and Engineering 2024-05-19T09:24:13Z 2024-05-19T09:24:13Z ©2024 2024-01 Thesis ID: 19301011 ID: 19301014 ID: 19301010 ID: 19301018 http://hdl.handle.net/10361/22875 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. 94 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Schizophrenia Logistic regression Mental illness Decision tree Language pattern Social media post Natural language processing GRU Bi-LSTM BERT Natural language processing (Computer science) Machine learning
spellingShingle	Schizophrenia Logistic regression Mental illness Decision tree Language pattern Social media post Natural language processing GRU Bi-LSTM BERT Natural language processing (Computer science) Machine learning Rodela, Raisa Rahman Efty, Farhan Tanvir Rahman, Mubashira Wajiha, Shaira Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
author2	Reza, Md Tanzim
author_facet	Reza, Md Tanzim Rodela, Raisa Rahman Efty, Farhan Tanvir Rahman, Mubashira Wajiha, Shaira
format	Thesis
author	Rodela, Raisa Rahman Efty, Farhan Tanvir Rahman, Mubashira Wajiha, Shaira
author_sort	Rodela, Raisa Rahman
title	Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
title_short	Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
title_full	Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
title_fullStr	Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
title_full_unstemmed	Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
title_sort	analyzing schizophrenic-prone text from social media content: a novel approach through ml and nlp
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22875
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Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP

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