Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak

Depression is a severe and pervasive threat to public health. These people like to express their thought, opinion and suggestion using social media network. Twitter is a popular microblogging site for users to post status updates (tweets). These tweets often reflect views on social issues, including...

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Bibliographic Details
Main Author:	Ishak, Najihah Salsabila
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Fuzzy arithmetic Online data processing
Online Access:	https://ir.uitm.edu.my/id/eprint/55293/1/55293.pdf
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id	my-uitm-ir.55293
record_format	uketd_dc
spelling	my-uitm-ir.552932022-01-25T06:54:58Z Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak 2021-02 Ishak, Najihah Salsabila Fuzzy arithmetic Online data processing Evolutionary programming (Computer science). Genetic algorithms Depression is a severe and pervasive threat to public health. These people like to express their thought, opinion and suggestion using social media network. Twitter is a popular microblogging site for users to post status updates (tweets). These tweets often reflect views on social issues, including psychological. Sentiment Analysis refers to natural language processing and text mining approaches to classify thoughts or sentiments from the tweet. Machine learning is an implementation of artificial intelligence (Al) that allows systems to learn and build on knowledge without being directly programmed automatically. This paper applies sentiment analysis, text mining, and machine learning to psychology to identify depression in Twitter user. The usefulness of using the user's tweet to measure depression studies using a literature review. The utility of current Python sentiment tools to a set of vocabulary used in microblogging is determined. The use of linguistic features to detect the sentiment in Twitter tweets are explored. A classifier model is developed using Naive Bayes characteristics. A comparison between built-in Scikit Learn Naive Bayes algorithm, and the scratch Naive Bayes algorithm is used to measure its effectiveness in terms of accuracy. At the end of this project, a prototype that can classify tweet is developed and used to monitor the tweets' sentiment probability. 2021-02 Thesis https://ir.uitm.edu.my/id/eprint/55293/ https://ir.uitm.edu.my/id/eprint/55293/1/55293.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Isa, Norulhidayah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Isa, Norulhidayah
topic	Fuzzy arithmetic Online data processing Fuzzy arithmetic
spellingShingle	Fuzzy arithmetic Online data processing Fuzzy arithmetic Ishak, Najihah Salsabila Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
description	Depression is a severe and pervasive threat to public health. These people like to express their thought, opinion and suggestion using social media network. Twitter is a popular microblogging site for users to post status updates (tweets). These tweets often reflect views on social issues, including psychological. Sentiment Analysis refers to natural language processing and text mining approaches to classify thoughts or sentiments from the tweet. Machine learning is an implementation of artificial intelligence (Al) that allows systems to learn and build on knowledge without being directly programmed automatically. This paper applies sentiment analysis, text mining, and machine learning to psychology to identify depression in Twitter user. The usefulness of using the user's tweet to measure depression studies using a literature review. The utility of current Python sentiment tools to a set of vocabulary used in microblogging is determined. The use of linguistic features to detect the sentiment in Twitter tweets are explored. A classifier model is developed using Naive Bayes characteristics. A comparison between built-in Scikit Learn Naive Bayes algorithm, and the scratch Naive Bayes algorithm is used to measure its effectiveness in terms of accuracy. At the end of this project, a prototype that can classify tweet is developed and used to monitor the tweets' sentiment probability.
format	Thesis
qualification_level	Bachelor degree
author	Ishak, Najihah Salsabila
author_facet	Ishak, Najihah Salsabila
author_sort	Ishak, Najihah Salsabila
title	Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
title_short	Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
title_full	Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
title_fullStr	Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
title_full_unstemmed	Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
title_sort	sentiment mining in twitter for early depression detection / najihah salsabila ishak
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/55293/1/55293.pdf
_version_	1783734911130140672

Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak

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