Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization

Government policy on a problematic topic can lead to pros and cons, including the implementation of work from home during the COVID-19 pandemic in Indonesia. Lots of social media users express their opinions through social media, such as Twitter. Using Twitter API, data on Twitter can be obtained fr...

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Autores principales:	Fatihah Rahmadayana, Yuliant Sibaroni
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	sentiment analysis randomized search hyperparameter tuning svm Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/086fa03965804db5866b0292e0dae635
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spelling	oai:doaj.org-article:086fa03965804db5866b0292e0dae6352021-11-16T13:16:11ZSentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization2580-076010.29207/resti.v5i5.3457https://doaj.org/article/086fa03965804db5866b0292e0dae6352021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3457https://doaj.org/toc/2580-0760Government policy on a problematic topic can lead to pros and cons, including the implementation of work from home during the COVID-19 pandemic in Indonesia. Lots of social media users express their opinions through social media, such as Twitter. Using Twitter API, data on Twitter can be obtained freely, so it can be utilized for sentiment analysis. Therefore, this study contains an analysis of public sentiment on the work from home policy using various preprocessing methods and Support Vector Machine with randomized search optimization. The result shows that the use of the acronym expansion method, slang word translation, and emoji translation in the preprocessing stage can increase the F1 Score value. The best F1 score results obtained were 83.362%. The results of the preprocessing method are used to predict unlabeled data. Prediction results show that 62.35% of tweets have positive sentiments, on the contrary, 37.65% of tweets have negative sentiments. So, it can conclude that most netizens support the policy of work from home.Fatihah RahmadayanaYuliant SibaroniIkatan Ahli Indormatika Indonesiaarticlesentiment analysisrandomized searchhyperparameter tuningsvmSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 936-942 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	sentiment analysis randomized search hyperparameter tuning svm Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	sentiment analysis randomized search hyperparameter tuning svm Systems engineering TA168 Information technology T58.5-58.64 Fatihah Rahmadayana Yuliant Sibaroni Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
description	Government policy on a problematic topic can lead to pros and cons, including the implementation of work from home during the COVID-19 pandemic in Indonesia. Lots of social media users express their opinions through social media, such as Twitter. Using Twitter API, data on Twitter can be obtained freely, so it can be utilized for sentiment analysis. Therefore, this study contains an analysis of public sentiment on the work from home policy using various preprocessing methods and Support Vector Machine with randomized search optimization. The result shows that the use of the acronym expansion method, slang word translation, and emoji translation in the preprocessing stage can increase the F1 Score value. The best F1 score results obtained were 83.362%. The results of the preprocessing method are used to predict unlabeled data. Prediction results show that 62.35% of tweets have positive sentiments, on the contrary, 37.65% of tweets have negative sentiments. So, it can conclude that most netizens support the policy of work from home.
format	article
author	Fatihah Rahmadayana Yuliant Sibaroni
author_facet	Fatihah Rahmadayana Yuliant Sibaroni
author_sort	Fatihah Rahmadayana
title	Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
title_short	Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
title_full	Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
title_fullStr	Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
title_full_unstemmed	Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization
title_sort	sentiment analysis of work from home activity using svm with randomized search optimization
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/086fa03965804db5866b0292e0dae635
work_keys_str_mv	AT fatihahrahmadayana sentimentanalysisofworkfromhomeactivityusingsvmwithrandomizedsearchoptimization AT yuliantsibaroni sentimentanalysisofworkfromhomeactivityusingsvmwithrandomizedsearchoptimization
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Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization

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