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Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language

This study explores the enhancement of accuracy in Indonesian sentiment analysis by incorporating text segmentation features during the pre-processing phase. One of the most important steps in creating a highquality Bag of Words is to separate Indonesian sentences with no spacing, which is made pos...

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Main Authors: Siti Mujilahwati, Siti Mujilahwati, M. Safar, Noor Zuraidin, Supriyanto, Catur
Format: Article
Language:English
Published: ASPG 2024
Subjects:
P302 - 302.87 Discourse analysis
Online Access:http://eprints.uthm.edu.my/12117/1/J17676_7aa8913f69426b15d1c915a823c6ac83.pdf
http://eprints.uthm.edu.my/12117/
https://doi.org/10.54216/FPA.150213
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spelling my.uthm.eprints.121172024-12-01T03:03:21Z http://eprints.uthm.edu.my/12117/ Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language Siti Mujilahwati, Siti Mujilahwati M. Safar, Noor Zuraidin Supriyanto, Catur P302 - 302.87 Discourse analysis This study explores the enhancement of accuracy in Indonesian sentiment analysis by incorporating text segmentation features during the pre-processing phase. One of the most important steps in creating a highquality Bag of Words is to separate Indonesian sentences with no spacing, which is made possible by the created text segmentation algorithm. Through the conducted observations and analyses, it was observed that text comments from social media frequently exhibit connected sentences without spacing. The segmentation process was developed through a matching model utilizing a standard Indonesian word dictionary. Implementation involved testing Indonesian text data related to COVID-19 management, resulting in a substantial increase of 3,036 features. The Bag of Words was then constructed using the Term Frequency-Inverse Document Frequency method. Subsequently, sentiment analysis classification testing was conducted using both deep learning and machine learning models to assess data quality and accuracy. The sentiment analysis accuracy for applying Deep Learning, Support Vector Machine and Naive Bayes is 86.46%, 88.02% and 86.19% respectively. ASPG 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12117/1/J17676_7aa8913f69426b15d1c915a823c6ac83.pdf Siti Mujilahwati, Siti Mujilahwati and M. Safar, Noor Zuraidin and Supriyanto, Catur (2024) Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language. Fusion: Practice and Applications (FPA), 15 (2). pp. 145-154. https://doi.org/10.54216/FPA.150213
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic P302 - 302.87 Discourse analysis
spellingShingle P302 - 302.87 Discourse analysis
Siti Mujilahwati, Siti Mujilahwati
M. Safar, Noor Zuraidin
Supriyanto, Catur
Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
description This study explores the enhancement of accuracy in Indonesian sentiment analysis by incorporating text segmentation features during the pre-processing phase. One of the most important steps in creating a highquality Bag of Words is to separate Indonesian sentences with no spacing, which is made possible by the created text segmentation algorithm. Through the conducted observations and analyses, it was observed that text comments from social media frequently exhibit connected sentences without spacing. The segmentation process was developed through a matching model utilizing a standard Indonesian word dictionary. Implementation involved testing Indonesian text data related to COVID-19 management, resulting in a substantial increase of 3,036 features. The Bag of Words was then constructed using the Term Frequency-Inverse Document Frequency method. Subsequently, sentiment analysis classification testing was conducted using both deep learning and machine learning models to assess data quality and accuracy. The sentiment analysis accuracy for applying Deep Learning, Support Vector Machine and Naive Bayes is 86.46%, 88.02% and 86.19% respectively.
format Article
author Siti Mujilahwati, Siti Mujilahwati
M. Safar, Noor Zuraidin
Supriyanto, Catur
author_facet Siti Mujilahwati, Siti Mujilahwati
M. Safar, Noor Zuraidin
Supriyanto, Catur
author_sort Siti Mujilahwati, Siti Mujilahwati
title Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
title_short Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
title_full Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
title_fullStr Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
title_full_unstemmed Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language
title_sort segmentation word to improve performance sentiment analysis for indonesian language
publisher ASPG
publishDate 2024
url http://eprints.uthm.edu.my/12117/1/J17676_7aa8913f69426b15d1c915a823c6ac83.pdf
http://eprints.uthm.edu.my/12117/
https://doi.org/10.54216/FPA.150213
_version_ 1817845167478013952
score 13.222552

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