Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction

The development of Web 2.0 has improved peoples’ ability to share their sentiments, or opinions, on various services or products easily. This is to investigate the public opinions that are expressed within the reviews. Aspect-based sentiment analysis (ABSA) deemed to receive a set of texts (e.g., pr...

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Main Author:	Abbas, Al Janabi Omar Mustafa
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75-76.95 Calculating Machines
Online Access:	http://eprints.usm.my/52458/1/AL%20JANABI%20OMAR%20MUSTAFA%20ABBAS%20-%20TESIS24.pdf
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id	my-usm-ep.52458
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spelling	my-usm-ep.524582022-04-30T15:40:06Z Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction 2021-10 Abbas, Al Janabi Omar Mustafa QA75-76.95 Calculating Machines The development of Web 2.0 has improved peoples’ ability to share their sentiments, or opinions, on various services or products easily. This is to investigate the public opinions that are expressed within the reviews. Aspect-based sentiment analysis (ABSA) deemed to receive a set of texts (e.g., product reviews or online reviews) and identify the opinion-target (aspect) within each review. Contemporary aspect-based sentiment analysis systems, like the aspect grouping, rely predominantly on lexicon-based and manually labelled seeds that is being incorporated into the topic models. The previously developed systems for Aspect Category Detection (ACD) rely mostly on supervised machine learning techniques. The problem of implicit aspect extraction is being addressed using either pre-constructed rules or pre-labelled clues for performing implicit aspect detection. To cope with these issues, Bayesian probabilistic models proposed to perform the aspect grouping, ACD, and distributed vectors for implicit aspect extraction. Parametric and non-parametric Bayesian models are developed to conduct both the annotated and non-annotated data, that are; Topic-seeds Latent Dirichlet allocation (TSLDA) and Hierarchical Dirichlet Process-Collapsed Gibbs Sampling (HDP-CGS), respectively. The yielded aspect groups using the developed Bayesian models fed into the advised distributed vector (i.e., Skip-gram) for implicit aspect extraction. The proposed methodology evaluated using several online reviews benchmark datasets (including datasets annotated using reviews retrieved from Amazon.com and TripAdvisor.com). 2021-10 Thesis http://eprints.usm.my/52458/ http://eprints.usm.my/52458/1/AL%20JANABI%20OMAR%20MUSTAFA%20ABBAS%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer - Tesis
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75-76.95 Calculating Machines
spellingShingle	QA75-76.95 Calculating Machines Abbas, Al Janabi Omar Mustafa Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
description	The development of Web 2.0 has improved peoples’ ability to share their sentiments, or opinions, on various services or products easily. This is to investigate the public opinions that are expressed within the reviews. Aspect-based sentiment analysis (ABSA) deemed to receive a set of texts (e.g., product reviews or online reviews) and identify the opinion-target (aspect) within each review. Contemporary aspect-based sentiment analysis systems, like the aspect grouping, rely predominantly on lexicon-based and manually labelled seeds that is being incorporated into the topic models. The previously developed systems for Aspect Category Detection (ACD) rely mostly on supervised machine learning techniques. The problem of implicit aspect extraction is being addressed using either pre-constructed rules or pre-labelled clues for performing implicit aspect detection. To cope with these issues, Bayesian probabilistic models proposed to perform the aspect grouping, ACD, and distributed vectors for implicit aspect extraction. Parametric and non-parametric Bayesian models are developed to conduct both the annotated and non-annotated data, that are; Topic-seeds Latent Dirichlet allocation (TSLDA) and Hierarchical Dirichlet Process-Collapsed Gibbs Sampling (HDP-CGS), respectively. The yielded aspect groups using the developed Bayesian models fed into the advised distributed vector (i.e., Skip-gram) for implicit aspect extraction. The proposed methodology evaluated using several online reviews benchmark datasets (including datasets annotated using reviews retrieved from Amazon.com and TripAdvisor.com).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abbas, Al Janabi Omar Mustafa
author_facet	Abbas, Al Janabi Omar Mustafa
author_sort	Abbas, Al Janabi Omar Mustafa
title	Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
title_short	Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
title_full	Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
title_fullStr	Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
title_full_unstemmed	Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction
title_sort	domain-independent bayesian model for aspect category detection and distributed vector for implicit aspect extraction
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer - Tesis
publishDate	2021
url	http://eprints.usm.my/52458/1/AL%20JANABI%20OMAR%20MUSTAFA%20ABBAS%20-%20TESIS24.pdf
_version_	1747822183701282816

Domain-independent Bayesian Model For Aspect Category Detection And Distributed Vector For Implicit Aspect Extraction

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