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Depression detection based on twitter using NLP and sentiment analysis

Link to publisher's homepage at https://amci.unimap.edu.my/

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Main Authors: Zuriani Hayati, Abdullah, Zheng, Lim Yam, Nabilah Filzah, Mohd Radzuan
Other Authors: zuriani.abdullah@newinti.edu.my
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2023
Subjects:
NLP
CNN
LSTM
Sentiment analysis
Social media
Twitter
Depression
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77716
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spelling my.unimap-777162023-01-25T04:03:46Z Depression detection based on twitter using NLP and sentiment analysis Zuriani Hayati, Abdullah Zheng, Lim Yam Nabilah Filzah, Mohd Radzuan zuriani.abdullah@newinti.edu.my Centre for Emerging Technologies in Computing (CETC), Faculty of Information Technology, INTI International University Faculty of Computing, Universiti4 Malaysia Pahang NLP CNN LSTM Sentiment analysis Social media Twitter Depression Link to publisher's homepage at https://amci.unimap.edu.my/ Depression is the most common illness, serious disease, and underestimated by human beings. The serious depression will affect the emotion, physical condition, or cause suicide. Depression can be detected by reading their social media post. This research aims to develop a system that used to analyze the user depression status based on their social media post. This research will implement Recurrent Neural Network (RNN) model and Convolutional Neural Network (CNN) model in order to get the most accurate parameter for building the model and compare the accuracy of the prediction. The RNN (LSTM) 7-layer model are the most accuracy, precision, recall, F1 score of and less loss compare with other three model. The accuracy is 80.99%, F1 80.16%, and loss 45.0%. The RNN (LSTM) had selected 7-layer as the model in development the google chrome extension to perform the tweet sentiment analysis. The system will notify the user about their depression status; suggested to ask treatment with phycologist. 2023-01-25T04:03:46Z 2023-01-25T04:03:46Z 2022-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 45-60 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77716 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic NLP
CNN
LSTM
Sentiment analysis
Social media
Twitter
Depression
spellingShingle NLP
CNN
LSTM
Sentiment analysis
Social media
Twitter
Depression
Zuriani Hayati, Abdullah
Zheng, Lim Yam
Nabilah Filzah, Mohd Radzuan
Depression detection based on twitter using NLP and sentiment analysis
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 zuriani.abdullah@newinti.edu.my
author_facet zuriani.abdullah@newinti.edu.my
Zuriani Hayati, Abdullah
Zheng, Lim Yam
Nabilah Filzah, Mohd Radzuan
format Article
author Zuriani Hayati, Abdullah
Zheng, Lim Yam
Nabilah Filzah, Mohd Radzuan
author_sort Zuriani Hayati, Abdullah
title Depression detection based on twitter using NLP and sentiment analysis
title_short Depression detection based on twitter using NLP and sentiment analysis
title_full Depression detection based on twitter using NLP and sentiment analysis
title_fullStr Depression detection based on twitter using NLP and sentiment analysis
title_full_unstemmed Depression detection based on twitter using NLP and sentiment analysis
title_sort depression detection based on twitter using nlp and sentiment analysis
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2023
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77716
_version_ 1772813100385304576
score 13.214268

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