Exploring classification for sentiment analysis from halal based tweets

Globally, social media is gaining popularity and redefining how people interact with one another online. Malaysian individuals, for example, are increasingly reliant on social media platforms such as Facebook and Twitter as well as LinkedIn, Pinterest, Instagram, and other similar sites. C...

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Main Authors: Setik, Roziyani, Raja Lope Ahmad, Raja Mohd Tariqi, Marjudi, Suziyanti
Format: Other
Language:English
Published: ResearchGate 2021
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Online Access:http://eprints.uthm.edu.my/6713/1/P13612_672eb4b3c3f7220482fcda8be619a60a.pdf
http://eprints.uthm.edu.my/6713/
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Institution: Universiti Tun Hussein Onn Malaysia
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spelling my.uthm.eprints.67132022-03-14T02:16:27Z http://eprints.uthm.edu.my/6713/ Exploring classification for sentiment analysis from halal based tweets Setik, Roziyani Raja Lope Ahmad, Raja Mohd Tariqi Marjudi, Suziyanti H Social Sciences (General) Globally, social media is gaining popularity and redefining how people interact with one another online. Malaysian individuals, for example, are increasingly reliant on social media platforms such as Facebook and Twitter as well as LinkedIn, Pinterest, Instagram, and other similar sites. Consider sentiment analysis to be a sub-category of social listening. A social media sentiment analysis has uncovered the public's current feelings on a particular topic or brand. Sentiment analysis is a technique for characterizing and capturing emotional states from unstructured text. The most important part of sentiment analysis is to evaluate a body of text to comprehend the opinion expressed by it. It usually assigns a polarity of “positive”, “negative” or “neutral”. It uses an algorithmic technique to capture people's thoughts, sentiments, and emotions by incorporating Natural Language Processing and Machine Learning technology. Sentiment analysis in Malaysia's social media is challenging to perform since posts are frequently written in a mixed language, usage of English and Malay with embedded jargon and various district dialect. The classification was performed based on Malaysia halal certification scheme for each tweet to acquire the class label's frequency value based on the sentiment analysis process's polarity results. It will demonstrate social media users' proclivity for posting and can act as a reference point for users when making decisions. An analysis of amounted 500 tweets with the hashtag #sijilhalal elicited information regarding people's feelings, preconceptions, and attitudes toward various issues related to halal certification in Malaysia. The discovery of a person's emotions concerning halal topics is visualized. Muslims' views are of importance to #sijilhalal awareness. ResearchGate 2021 Other NonPeerReviewed text en http://eprints.uthm.edu.my/6713/1/P13612_672eb4b3c3f7220482fcda8be619a60a.pdf Setik, Roziyani and Raja Lope Ahmad, Raja Mohd Tariqi and Marjudi, Suziyanti (2021) Exploring classification for sentiment analysis from halal based tweets. ResearchGate.
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 H Social Sciences (General)
spellingShingle H Social Sciences (General)
Setik, Roziyani
Raja Lope Ahmad, Raja Mohd Tariqi
Marjudi, Suziyanti
Exploring classification for sentiment analysis from halal based tweets
description Globally, social media is gaining popularity and redefining how people interact with one another online. Malaysian individuals, for example, are increasingly reliant on social media platforms such as Facebook and Twitter as well as LinkedIn, Pinterest, Instagram, and other similar sites. Consider sentiment analysis to be a sub-category of social listening. A social media sentiment analysis has uncovered the public's current feelings on a particular topic or brand. Sentiment analysis is a technique for characterizing and capturing emotional states from unstructured text. The most important part of sentiment analysis is to evaluate a body of text to comprehend the opinion expressed by it. It usually assigns a polarity of “positive”, “negative” or “neutral”. It uses an algorithmic technique to capture people's thoughts, sentiments, and emotions by incorporating Natural Language Processing and Machine Learning technology. Sentiment analysis in Malaysia's social media is challenging to perform since posts are frequently written in a mixed language, usage of English and Malay with embedded jargon and various district dialect. The classification was performed based on Malaysia halal certification scheme for each tweet to acquire the class label's frequency value based on the sentiment analysis process's polarity results. It will demonstrate social media users' proclivity for posting and can act as a reference point for users when making decisions. An analysis of amounted 500 tweets with the hashtag #sijilhalal elicited information regarding people's feelings, preconceptions, and attitudes toward various issues related to halal certification in Malaysia. The discovery of a person's emotions concerning halal topics is visualized. Muslims' views are of importance to #sijilhalal awareness.
format Other
author Setik, Roziyani
Raja Lope Ahmad, Raja Mohd Tariqi
Marjudi, Suziyanti
author_facet Setik, Roziyani
Raja Lope Ahmad, Raja Mohd Tariqi
Marjudi, Suziyanti
author_sort Setik, Roziyani
title Exploring classification for sentiment analysis from halal based tweets
title_short Exploring classification for sentiment analysis from halal based tweets
title_full Exploring classification for sentiment analysis from halal based tweets
title_fullStr Exploring classification for sentiment analysis from halal based tweets
title_full_unstemmed Exploring classification for sentiment analysis from halal based tweets
title_sort exploring classification for sentiment analysis from halal based tweets
publisher ResearchGate
publishDate 2021
url http://eprints.uthm.edu.my/6713/1/P13612_672eb4b3c3f7220482fcda8be619a60a.pdf
http://eprints.uthm.edu.my/6713/
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