ANALISIS SENTIMEN TRANSFER PEMAIN SEPAK BOLA MENGGUNAKAN DEEP LEARNING ROBERTA

RIDHO, M ALI and Abdiansah, Abdiansah and Yusliani, Novi (2023) ANALISIS SENTIMEN TRANSFER PEMAIN SEPAK BOLA MENGGUNAKAN DEEP LEARNING ROBERTA. Undergraduate thesis, Sriwijaya University.

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Abstract

Football transfer can impact performance of a club, both on or off the pitch. By analyzing sentiment surrounding player signing, clubs can enhance their marketing strategy to achieve higher earnings. Using 1200 data from comment across some of world class player signing announcements on Twitter, this study aims to perform sentiment analysis task using RoBERTa Model on said data. To adapt RoBERTa model for sentiment analysis, the pretrained RoBERTa base model is modified with additional output layer and then the model is fine-tuned. Testing model revealed that the best accuracy is produced by using epoch value 6 and learning rate value 10-5, resulting in 88,00% accuracy, 89,58% precision, 86,00% recall and 87,75% f-measure. However, there is sign of overfitting during fine-tuning model with learning rate value 10-5 that started to appear at epoch 4. Meanwhile, even though model with learning rate value 10-6 did not indicate any overfit sign, this configuration started slower even in third epoch their accuracy is still only at 50,00%. Eventually, model with learning rate value 10-6 caught up and achieved its best accuracy 85,50% with epoch value 8. Although it is the best for learning rate value 10-6, this accuracy score is still 1,50% behind the lowest accuracy for learning rate value 10-5 that achieve 86,00% accuracy with only 3 epochs. Therefore, it can be concluded that using higher learning rate will result in much faster learning process though it can also drive the model to overfit. Additionally, increase epoch also can increase accuracy but the rate of improvement may vary depending on the learning rate.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, Football Player, Transfer, Deep Learning, RoBERTa
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	M Ali Ridho
Date Deposited:	14 Aug 2023 03:18
Last Modified:	14 Aug 2023 03:18
URI:	http://repository.unsri.ac.id/id/eprint/126992

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