Slangs And Short Forms Of Malay Twitter Sentiment Analysis Using Supervised Machine Learning
The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
International Journal of Computer Science and Network Security (IJCSNS)
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25548/2/2.3.1.1.2%20IJCSNS-SLANGS%20AND%20SHORT%20FORMS%20OF%20MALAY%20TWITTER%20SENTIMENT%20ANALYSIS%20USING%20SUPERVISED%20MACHINE%20LEARNING.PDF http://eprints.utem.edu.my/id/eprint/25548/ http://paper.ijcsns.org/07_book/202111/20211140.pdf |
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| Summary: | The current society relies upon social media on an everyday basis,
which contributes to finding which of the following supervised
machine learning algorithms used in sentiment analysis have
higher accuracy in detecting Malay internet slang and short forms
which can be offensive to a person. This paper is to determine
which of the algorithms chosen in supervised machine learning
with higher accuracy in detecting internet slang and short forms.
To analyze the results of the supervised machine learning
classifiers, we have chosen two types of datasets, one is political
topic-based, and another same set but is mixed with 50 tweets per
targeted keyword. The datasets are then manually labelled
positive and negative, before separating the 275 tweets into
training and testing sets. Naïve Bayes and Random Forest
classifiers are then analyzed and evaluated from their
performances. Our experiment results show that Random Forest
is a better classifier compared to Naïve Bayes. |
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