KLASIFIKASI EMOSI PADA TWITTER MENGGUNAKAN BIDIRECTIONAL LONG SHORT TERM MEMORY

PANE, RACHEL and Abdiansah, Abdiansah and Rodiah, Desty (2023) KLASIFIKASI EMOSI PADA TWITTER MENGGUNAKAN BIDIRECTIONAL LONG SHORT TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

Twitter is one of the social media platforms where users can share messages. These messages often contain emotions. The conveyed emotions in these tweets can be recognized through an emotion classification process. However, the text data in tweets is often unstructured, so the appropriate method needs to be applied for emotion classification. The method used in this research involves the utilization of Deep Learning algorithm, specifically Bidirectional Long Short Term Memory (Bi-LSTM), which is an extension of the Long Short Term Memory (LSTM) method for emotion classification. The emotion classification process in this research utilizes 3185 data, which is then divided into 80% training data and 20% testing data. Additionally, the use of Word2Vec word embedding is implemented to maximize the results. After tuning the hyperparameters using random search, the best final result for the Bi-LSTM model is obtained with a dropout layer of 0.2, hidden units of 128, dropout in the Bi-LSTM layer of 0.3, recurrent dropout of 0.4, 10 epochs, and a batch size of 32. The final evaluation results reach an accuracy of 78% with macro precision, macro recall, and macro F-Measure averaging at 79%, 78%, and 78% respectively. Similarly, the weighted precision, weighted recall, and weighted F-Measure reach 79%, 78%, and 78% respectively, which is considered good for analyzing emotions in Twitter texts.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Emotion Classification, Twitter, Word2Vec, Bidirectional Long Short Term Memory
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Rachel Pane
Date Deposited:	10 Jul 2023 05:12
Last Modified:	10 Jul 2023 05:12
URI:	http://repository.unsri.ac.id/id/eprint/115161

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