Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews

The unavailability of labelled data for a particular domain poses a challenge for training a classifier for sentiment detection in product reviews. Cross-domain sentiment analysis offers a solution to train models using labelled data from source domains and applying it to the target domain. However,...

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Bibliographic Details
Main Author:	Gupta, Aditi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/60238/1/24%20Pages%20from%20ADITI%20GUPTA.pdf
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id	my-usm-ep.60238
record_format	uketd_dc
spelling	my-usm-ep.602382024-03-20T03:20:33Z Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews 2023-04 Gupta, Aditi QA75.5-76.95 Electronic computers. Computer science The unavailability of labelled data for a particular domain poses a challenge for training a classifier for sentiment detection in product reviews. Cross-domain sentiment analysis offers a solution to train models using labelled data from source domains and applying it to the target domain. However, the classifier performance usually suffers significantly when the source and target domains’ feature distribution and sentiment expressions differ. Also, when using multiple source domains, not all source domains are equally beneficial as some are more relevant to a particular target domain. This thesis addresses these issues by developing cross-domain deep learning classifiers and investigating the impact of multiple source domains on sentiment classifier training. Furthermore, the effect of each source domain on the training of the cross-domain sentiment classifier and selecting helpful source domains is examined. The study developed a novel method of assigning weights, to each source domain according to its importance to the target domain. A three-phase methodology is implemented, with Phase 1 focusing on creating the deep learning architecture using CNN with optimal hyperparameters for cross-domain classification tasks followed by extensive experiments to find the relevance between various source domains to the target domain. 2023-04 Thesis http://eprints.usm.my/60238/ http://eprints.usm.my/60238/1/24%20Pages%20from%20ADITI%20GUPTA.pdf application/pdf en public phd doctoral Perpustakaan Hamzah Sendut Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Gupta, Aditi Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
description	The unavailability of labelled data for a particular domain poses a challenge for training a classifier for sentiment detection in product reviews. Cross-domain sentiment analysis offers a solution to train models using labelled data from source domains and applying it to the target domain. However, the classifier performance usually suffers significantly when the source and target domains’ feature distribution and sentiment expressions differ. Also, when using multiple source domains, not all source domains are equally beneficial as some are more relevant to a particular target domain. This thesis addresses these issues by developing cross-domain deep learning classifiers and investigating the impact of multiple source domains on sentiment classifier training. Furthermore, the effect of each source domain on the training of the cross-domain sentiment classifier and selecting helpful source domains is examined. The study developed a novel method of assigning weights, to each source domain according to its importance to the target domain. A three-phase methodology is implemented, with Phase 1 focusing on creating the deep learning architecture using CNN with optimal hyperparameters for cross-domain classification tasks followed by extensive experiments to find the relevance between various source domains to the target domain.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Gupta, Aditi
author_facet	Gupta, Aditi
author_sort	Gupta, Aditi
title	Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
title_short	Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
title_full	Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
title_fullStr	Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
title_full_unstemmed	Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews
title_sort	similarity-based weights for cross-domain sentiment classification of product reviews
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Sains Komputer
publishDate	2023
url	http://eprints.usm.my/60238/1/24%20Pages%20from%20ADITI%20GUPTA.pdf
_version_	1794024111660335104

Similarity-Based Weights For Cross-Domain Sentiment Classification Of Product Reviews

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