PENGARUH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) PADA SENTIMEN ANALISIS MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER

POLINGAY, NURMASITA ANAWULA MUSVITASARI and Yusliani, Novi and Marieska, Mastura Diana (2020) PENGARUH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) PADA SENTIMEN ANALISIS MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis is a multidisciplinary field of study that is used to analyze people's sentiments towards an entity. Sentiment analysis can be coNducted using Twitter data from social media Twitter. However, the amount of tweet data is sometimes unbalanced, where the amount of data is more in one class compared to other classes or what is known as imbalanced data. Naïve Bayes is an algorithm that can be used to analyze a sentiment. However, Naïve Bayes itself is not equipped to solve the problem of imbalanced data. To solve this problem, one approach can be done by applying SMOTE (Synthetic Minority Oversampling Technique). From the test results using four different datasets, it shows that the application of SMOTE by synthesizing data in the minor class in sentiment analysis using the Naïve Bayes algorithm has an effect with an average increase of accuracy is 5%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis sentimen, imbalanced data, Naïve Bayes, SMOTE (Synthetic Minority Oversampling Technique).
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9975 not found.
Date Deposited:	19 Jan 2021 06:01
Last Modified:	20 Jan 2021 03:08
URI:	http://repository.unsri.ac.id/id/eprint/40414

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