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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
http://eprints.usm.my/60433/
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spelling my.usm.eprints.60433 http://eprints.usm.my/60433/ English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment 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 NonPeerReviewed application/pdf en http://eprints.usm.my/60433/1/Pages%20from%20LIM%20YING%20HAO%20-%20TESIS.pdf Lim, Ying Hao (2023) English-Malay Cross-Lingual Emotion Detection In Tweets Using Word Embedding Alignment. Masters thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
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
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
publishDate 2023
url http://eprints.usm.my/60433/1/Pages%20from%20LIM%20YING%20HAO%20-%20TESIS.pdf
http://eprints.usm.my/60433/
_version_ 1797907865786122240
score 13.211869

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