Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis

There have been 350,000 tweets generated by the interaction of social networks with different cultures and educational backgrounds in the last ten years. Various sentiments are expressed in the user comments, from support to hatred. The sentiments regarded the United States General Election in 2020....

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Autores principales:	Sandi Hermawan, Rilla Mandala
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	cnn-bilstm relu sentiment analysis serlu us election 2020 Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/47a0ebe4b47846e580ef85bd6c100f6f
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spelling	oai:doaj.org-article:47a0ebe4b47846e580ef85bd6c100f6f2021-11-16T13:16:11ZImproving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis2580-076010.29207/resti.v5i5.3534https://doaj.org/article/47a0ebe4b47846e580ef85bd6c100f6f2021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3534https://doaj.org/toc/2580-0760There have been 350,000 tweets generated by the interaction of social networks with different cultures and educational backgrounds in the last ten years. Various sentiments are expressed in the user comments, from support to hatred. The sentiments regarded the United States General Election in 2020. This dataset has 3,000 data gotten from previous research. We augment it becomes 15,000 data to facilitate training and increase the required data. Sentiment detection is carried out using the CNN-BiLSTM architecture. It is chosen because CNN can filter essential words, and BiLSTM can remember memory in two directions. By utilizing both, the training process becomes maximum. However, this method has disadvantages in the activation. The drawback of the existing activation method, i.e., "Zero-hard Rectifier" and "ReLU Dropout" problem to become the cause of training stopped in the ReLU activation, and the exponential function cannot be set become the activation function still rigid towards output value in the SERLU activation. To overcome this problem, we propose a novel activation method to repair activation in CNN-BiLSTM architecture. It is namely the ASERLU activation function. It can adjust positive value output, negative value output, and exponential value by the setter variables. So, it adapts more conveniently to the output value and becomes a flexible activation function because it can be increased and decreased as needed. It is the first research applied in architecture. Compared with ReLU and SERLU, our proposed method gives higher accuracy based on the experiment results.Sandi HermawanRilla MandalaIkatan Ahli Indormatika Indonesiaarticlecnn-bilstmrelusentiment analysisserluus election 2020Systems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 1001-1007 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	cnn-bilstm relu sentiment analysis serlu us election 2020 Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	cnn-bilstm relu sentiment analysis serlu us election 2020 Systems engineering TA168 Information technology T58.5-58.64 Sandi Hermawan Rilla Mandala Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
description	There have been 350,000 tweets generated by the interaction of social networks with different cultures and educational backgrounds in the last ten years. Various sentiments are expressed in the user comments, from support to hatred. The sentiments regarded the United States General Election in 2020. This dataset has 3,000 data gotten from previous research. We augment it becomes 15,000 data to facilitate training and increase the required data. Sentiment detection is carried out using the CNN-BiLSTM architecture. It is chosen because CNN can filter essential words, and BiLSTM can remember memory in two directions. By utilizing both, the training process becomes maximum. However, this method has disadvantages in the activation. The drawback of the existing activation method, i.e., "Zero-hard Rectifier" and "ReLU Dropout" problem to become the cause of training stopped in the ReLU activation, and the exponential function cannot be set become the activation function still rigid towards output value in the SERLU activation. To overcome this problem, we propose a novel activation method to repair activation in CNN-BiLSTM architecture. It is namely the ASERLU activation function. It can adjust positive value output, negative value output, and exponential value by the setter variables. So, it adapts more conveniently to the output value and becomes a flexible activation function because it can be increased and decreased as needed. It is the first research applied in architecture. Compared with ReLU and SERLU, our proposed method gives higher accuracy based on the experiment results.
format	article
author	Sandi Hermawan Rilla Mandala
author_facet	Sandi Hermawan Rilla Mandala
author_sort	Sandi Hermawan
title	Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
title_short	Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
title_full	Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
title_fullStr	Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
title_full_unstemmed	Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis
title_sort	improving accuracy using the aserlu layer in cnn-bilstm architecture on sentiment analysis
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/47a0ebe4b47846e580ef85bd6c100f6f
work_keys_str_mv	AT sandihermawan improvingaccuracyusingtheaserlulayerincnnbilstmarchitectureonsentimentanalysis AT rillamandala improvingaccuracyusingtheaserlulayerincnnbilstmarchitectureonsentimentanalysis
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Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis

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