Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman

This research delves into the widespread issue of domestic violence, emphasizing its severe impact on individuals and society globally. The surge in domestic violence during the COVID-19 pandemic, as highlighted by UN Women's survey, particularly in countries like Kenya, sets the stage for the...

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Main Author:	Mohd Rahiman, Nurulizzah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/96468/1/96468.pdf
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id	my-uitm-ir.96468
record_format	uketd_dc
spelling	my-uitm-ir.964682024-06-06T03:34:38Z Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman 2024 Mohd Rahiman, Nurulizzah Algorithms This research delves into the widespread issue of domestic violence, emphasizing its severe impact on individuals and society globally. The surge in domestic violence during the COVID-19 pandemic, as highlighted by UN Women's survey, particularly in countries like Kenya, sets the stage for the research problem. Recognizing the lack of public awareness and understanding of attitudes towards domestic violence, the study proposes using sentiment analysis on Twitter data to monitor real-time public sentiment. The research objectives focus on studying and applying the Naive Bayes algorithm for sentiment analysis on tweets related to domestic violence, aiming to provide insights for researchers, government agencies, policymakers, and the public and develop a prediction model using Naive Bayes algorithm to evaluate its performance. The scope involves using English language tweets collected from March 2021 to November 2023, limiting the data to the topic of domestic violence. Few Naive Bayes classifiers are used to compare the accuracy of the Naive Bayes algorithm and parameter tuning also done on the classifiers. Resampling is used to handle the imbalance dataset. This research also compares using VADER and SentiWordNet lexicon to compare which has the best accuracy. The evaluation of algorithms consists of comparing the accuracy, specificity, and other evaluation metrics. Based on the results, Bernoulli classifier has the best accuracy of 94% while Multinomial has an accuracy of 93%. The best ratio of data to be used are 80:20 with VADER lexicon approach. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96468/ https://ir.uitm.edu.my/id/eprint/96468/1/96468.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Mohamed Yusoff, Syarifah Adilah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohamed Yusoff, Syarifah Adilah
topic	Algorithms
spellingShingle	Algorithms Mohd Rahiman, Nurulizzah Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
description	This research delves into the widespread issue of domestic violence, emphasizing its severe impact on individuals and society globally. The surge in domestic violence during the COVID-19 pandemic, as highlighted by UN Women's survey, particularly in countries like Kenya, sets the stage for the research problem. Recognizing the lack of public awareness and understanding of attitudes towards domestic violence, the study proposes using sentiment analysis on Twitter data to monitor real-time public sentiment. The research objectives focus on studying and applying the Naive Bayes algorithm for sentiment analysis on tweets related to domestic violence, aiming to provide insights for researchers, government agencies, policymakers, and the public and develop a prediction model using Naive Bayes algorithm to evaluate its performance. The scope involves using English language tweets collected from March 2021 to November 2023, limiting the data to the topic of domestic violence. Few Naive Bayes classifiers are used to compare the accuracy of the Naive Bayes algorithm and parameter tuning also done on the classifiers. Resampling is used to handle the imbalance dataset. This research also compares using VADER and SentiWordNet lexicon to compare which has the best accuracy. The evaluation of algorithms consists of comparing the accuracy, specificity, and other evaluation metrics. Based on the results, Bernoulli classifier has the best accuracy of 94% while Multinomial has an accuracy of 93%. The best ratio of data to be used are 80:20 with VADER lexicon approach.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Rahiman, Nurulizzah
author_facet	Mohd Rahiman, Nurulizzah
author_sort	Mohd Rahiman, Nurulizzah
title	Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
title_short	Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
title_full	Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
title_fullStr	Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
title_full_unstemmed	Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman
title_sort	sentiment analysis of domestic violence prediction using naive bayes algorithm / nurulizzah mohd rahiman
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96468/1/96468.pdf
_version_	1804889990047465472

Sentiment analysis of domestic violence prediction using Naive Bayes algorithm / Nurulizzah Mohd Rahiman

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