KLASIFIKASI EMOSI PADA TWITTER DENGAN METODE EMOTION LEXICON DAN NAIVE BAYES

DZAHABIYYAH, DHIYA FAIRUZ RAY and Yusliani, Novi and Miraswan, Kanda Januar (2021) KLASIFIKASI EMOSI PADA TWITTER DENGAN METODE EMOTION LEXICON DAN NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion of a sentence is the Emotion Lexicon. However, the lexicon based method is not good at classifying data because not every word contains emotion, so it cannot be used as a specific result and a better classification method is needed, namely combining Naïve Bayes with Emotion Lexicon. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f-measure of 59%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Emosi, Emotion Lexicon, Klasifikasi, Naïve Bayes, Twitter
Subjects: B Philosophy. Psychology. Religion > BF Psychology > BF511-593 Affection. Feeling. Emotion
P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
T Technology > T Technology (General) > T1-995 Technology (General) > T14 Philosophy. Theory. Classification. Methodology Cf. CB478 Technology and civilization
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Dhiya Fairuz Ray Dzahabiyyah
Date Deposited: 28 Jul 2021 08:42
Last Modified: 28 Jul 2021 08:42
URI: http://repository.unsri.ac.id/id/eprint/50820

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