Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan

With pandemic covid-19 getting worse and most people need to be stayed at home, will surely make rate of mental health conditioner to be increased. Mental health shouldn't be taken lightly since the sole reason of suicide come from underlying mental health problems. But since most people need t...

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Main Author:	Ruslan, Mohd Tharwan Hadi
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Instruments and machines Neural networks (Computer science) Analytic mechanics
Online Access:	https://ir.uitm.edu.my/id/eprint/55140/1/55140.pdf
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id	my-uitm-ir.55140
record_format	uketd_dc
spelling	my-uitm-ir.551402023-10-26T00:11:40Z Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan 2021-02 Ruslan, Mohd Tharwan Hadi Instruments and machines Electronic Computers. Computer Science Neural networks (Computer science) Analytic mechanics With pandemic covid-19 getting worse and most people need to be stayed at home, will surely make rate of mental health conditioner to be increased. Mental health shouldn't be taken lightly since the sole reason of suicide come from underlying mental health problems. But since most people need to stay at home, then by no mean detecting mental health patient will be difficult. So, the only way left to detect anxiety patients is through online or specifically social media. Since anxiety is the leading condition among all mental health condition, beating depression even, then this project will focus on detecting anxiety patient through Twitter tweets. The reason why this project will use social media Twitter to detect anxiety is because these anxiety conditioners sometime will post that they having anxiety or nearly having anxiety on social media. So this project aim to detect anxiety early through social media Twitter. This project surely be able to help the society, by giving mental health facility the capability to detect anxiety patient through Twitter, contact them early, and giving them early treatments. The system will get the data from the user, which in this case is mental health staff, and then decide whether the data received is positive anxiety or not. This project will consist of literature study, data collection, data analysis, data cleaning, user interface design and classifier design. From literature study, the CNN algorithm had been chosen for this project, since CNN algorithm proof to be the best algorithm for classifying anxiety patients. Twitter API and tweepy will be used for data collection and textblob for sentiment analysis. Then there will be a lot of preprocess step to clean the data. As for the classifier design, the keras function will be used to generate CNN classifier. And python GUI for user interface. Model and system evaluation are also done for the project, proof that the classifier and user interface able to function as intended. In conclusion, this project may help decreasing the mental health patients, hence decrease the suicide rate. 2021-02 Thesis https://ir.uitm.edu.my/id/eprint/55140/ https://ir.uitm.edu.my/id/eprint/55140/1/55140.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Jantan, Hamidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jantan, Hamidah
topic	Instruments and machines Instruments and machines Neural networks (Computer science) Analytic mechanics
spellingShingle	Instruments and machines Instruments and machines Neural networks (Computer science) Analytic mechanics Ruslan, Mohd Tharwan Hadi Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
description	With pandemic covid-19 getting worse and most people need to be stayed at home, will surely make rate of mental health conditioner to be increased. Mental health shouldn't be taken lightly since the sole reason of suicide come from underlying mental health problems. But since most people need to stay at home, then by no mean detecting mental health patient will be difficult. So, the only way left to detect anxiety patients is through online or specifically social media. Since anxiety is the leading condition among all mental health condition, beating depression even, then this project will focus on detecting anxiety patient through Twitter tweets. The reason why this project will use social media Twitter to detect anxiety is because these anxiety conditioners sometime will post that they having anxiety or nearly having anxiety on social media. So this project aim to detect anxiety early through social media Twitter. This project surely be able to help the society, by giving mental health facility the capability to detect anxiety patient through Twitter, contact them early, and giving them early treatments. The system will get the data from the user, which in this case is mental health staff, and then decide whether the data received is positive anxiety or not. This project will consist of literature study, data collection, data analysis, data cleaning, user interface design and classifier design. From literature study, the CNN algorithm had been chosen for this project, since CNN algorithm proof to be the best algorithm for classifying anxiety patients. Twitter API and tweepy will be used for data collection and textblob for sentiment analysis. Then there will be a lot of preprocess step to clean the data. As for the classifier design, the keras function will be used to generate CNN classifier. And python GUI for user interface. Model and system evaluation are also done for the project, proof that the classifier and user interface able to function as intended. In conclusion, this project may help decreasing the mental health patients, hence decrease the suicide rate.
format	Thesis
qualification_level	Bachelor degree
author	Ruslan, Mohd Tharwan Hadi
author_facet	Ruslan, Mohd Tharwan Hadi
author_sort	Ruslan, Mohd Tharwan Hadi
title	Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
title_short	Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
title_full	Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
title_fullStr	Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
title_full_unstemmed	Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan
title_sort	early detection of anxiety in social media using convolution neural network / mohd tharwan hadi ruslan
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/55140/1/55140.pdf
_version_	1783734904835538944

Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan

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