English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment

English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment

The three-phase methodology to address the goals of this study included the construction of the English-Malay cross-lingual word embedding using word embedding alignment, enrichment of the cross-lingual word embedding with sentiment information, and pre-training of the hierarchical attention model s...

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Main Author: Lim, Ying Hao
Format: Thesis
Language:English
Published: 2023
Subjects:
QA75.5-76.95 Electronic computers
Computer science
Online Access:http://eprints.usm.my/60433/1/Pages%20from%20LIM%20YING%20HAO%20-%20TESIS.pdf
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spelling my-usm-ep.604332024-04-26T08:01:52Z English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment 2023-03 Lim, Ying Hao QA75.5-76.95 Electronic computers. Computer science The three-phase methodology to address the goals of this study included the construction of the English-Malay cross-lingual word embedding using word embedding alignment, enrichment of the cross-lingual word embedding with sentiment information, and pre-training of the hierarchical attention model solely on English tweets. We evaluated our model in two scenarios: zero-shot learning and few-shot learning on 4176 Malay tweets annotated with emotion. We also examined the optimal number of Malay tweets required to finetune the model and the effect of finetuning different layers in our model. 2023-03 Thesis http://eprints.usm.my/60433/ http://eprints.usm.my/60433/1/Pages%20from%20LIM%20YING%20HAO%20-%20TESIS.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Komputer ( School of Computer Sciences)
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
Lim, Ying Hao
English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
description The three-phase methodology to address the goals of this study included the construction of the English-Malay cross-lingual word embedding using word embedding alignment, enrichment of the cross-lingual word embedding with sentiment information, and pre-training of the hierarchical attention model solely on English tweets. We evaluated our model in two scenarios: zero-shot learning and few-shot learning on 4176 Malay tweets annotated with emotion. We also examined the optimal number of Malay tweets required to finetune the model and the effect of finetuning different layers in our model.
format Thesis
qualification_level Master's degree
author Lim, Ying Hao
author_facet Lim, Ying Hao
author_sort Lim, Ying Hao
title English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
title_short English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
title_full English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
title_fullStr English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
title_full_unstemmed English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment
title_sort english-malay cross-lingual emotion detection in tweets using word embedding alignment
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Sains Komputer ( School of Computer Sciences)
publishDate 2023
url http://eprints.usm.my/60433/1/Pages%20from%20LIM%20YING%20HAO%20-%20TESIS.pdf
_version_ 1804888936157282304

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