Exploring classification for sentiment analysis from halal based tweets

Setik, Roziyani and Raja Lope Ahmad, Raja Mohd Tariqi and Marjudi, Suziyanti (2021) Exploring classification for sentiment analysis from halal based tweets. ResearchGate.

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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.

Item Type: Other
Uncontrolled Keywords: Sentiment analysis; corpus, #sijilhalal; classification; NLP
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Faculty of Computer Science and Information Technology > Department of Information Security
Depositing User: Mr. Abdul Rahim Mat Radzuan
Date Deposited: 14 Mar 2022 02:16
Last Modified: 14 Mar 2022 02:16
URI: http://eprints.uthm.edu.my/id/eprint/6713

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