PENGARUH BRILL TAGGER TERHADAP HASIL KLASIFIKASI ANALISIS SENTIMEN MENGGUNAKAN ALGORITMA MULTINOMIAL NAIVE BAYES

NANDITO, R. ASTERO and Abdiansah, Abdiansah and Yusliani, Novi (2021) PENGARUH BRILL TAGGER TERHADAP HASIL KLASIFIKASI ANALISIS SENTIMEN MENGGUNAKAN ALGORITMA MULTINOMIAL NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

Twitter is a social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator for influence in research, the problem that arises in research in the field of sentiment analysis is the large number of factors such as the use of informal or colloquial language and other factors that can affect the results of sentiment classification. To improve the results of sentiment classification, an information extraction process can be carried out. One part of the information extraction feature is a part of speech tagging, which is the giving of word classes automatically. The results of part of speech tagging are used for weighting words based on part of speech. This study examines the effect of Part of Speech Tagging with the method Brill Tagger in sentiment analysis using the Naive Bayes Multinomial algorithm. Testing were carried out on 500 twitter tweet texts and obtained the results of the sentiment classification with implementing part of speech tagging precision 73,2%, recall 63,2%, f-measure 67,6%, accuracy 60,7% and without implementing part of speech tagging precision 65,2%, recall 60,6%, f-measure 62,4% accuracy 53,3%. From the results of the accuracy obtained, it shows that the application of part of speech tagging in sentiment analysis using the Multinomial Naïve Bayes algorithm has an effect with an increase in classification performance.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Sentimen, Pemrosesan Bahasa Alami
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9847 not found.
Date Deposited:	13 Jan 2021 04:55
Last Modified:	13 Jan 2021 04:55
URI:	http://repository.unsri.ac.id/id/eprint/39932

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