Peer Review: SENTIMENT ANALYSIS USING RECURRENT NEURAL NETWORK-LSTM IN BAHASA INDONESIA

Kurniasari, Lilis and Setyanto, Arif (2020) Peer Review: SENTIMENT ANALYSIS USING RECURRENT NEURAL NETWORK-LSTM IN BAHASA INDONESIA. Taylor's University.

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Abstract

The opinion of different individuals is an essential factor that needs to be considered in decision making. Due to the current competitive business environment, understanding customer opinions determine the success of modern companies. Various machine learning algorithms are used in Automatics opinion polarization mining from online text sources such as social media, user comments, and reviews. This approach has been used in many languages, including Indonesian. The purpose of this study therefore is to proposes the Recurrent Neural Network (RNN)-Long Short Memory Term (LSTM) in classifying sentiment polarity of Indonesian sentences. It evaluates the proposed algorithm with a dataset from traveling site reviews consisting of 25,000 reports in two classes of equal proportion (positive and negative). According to the evaluation results, the model has 95.0% accuracy.

Item Type:	Other
Uncontrolled Keywords:	Deep learning, Long short memory term, Neural network, Recurrent Neural Network, Sentiment analysis.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Tri Yuliani
Date Deposited:	22 Apr 2022 05:05
Last Modified:	28 Apr 2022 04:30
URI:	http://repository.unu-jogja.ac.id/id/eprint/168

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