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Attention-Based BiLSTM for Negation Handling in Sentimen Analysis

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 4 (2020) · pp. 397–406

DOI
10.22146/ijccs.60733
ISSN
2460-7258, 1978-1520
Published
2020-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.

Keywords

Collaboration network of the authors

13 authors · 18 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SihabuddinUniversitas Gadjah Mada2
Sensa Gudya Sauma SyahraUniversitas Gadjah Mada2
Sigit PriyantaUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Auliya Rahman IsnainUniversitas Teknokrat Indonesia1
Annisaa UtamiUniversitas Gadjah Mada1
Aina MusdholifahUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Fadhlan Pasyah Al FaridziUniversitas Gadjah Mada1
Fadila Shely AmaliaUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Attention-Based BiLSTM for Negation Handling in Sentimen Analysis</dc:title>
	<dc:creator>Pratiwi, Riszki Wijayatun</dc:creator>
	<dc:creator>Sari, Yunita</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:subject xml:lang="en-US">Natural Language Processing and Sentimen Analysis</dc:subject>
	<dc:subject xml:lang="en-US">LSTM; attention-based LSTM; BiLSTM; Attention based Negation BiLSTM; sentiment analysis</dc:subject>
	<dc:description xml:lang="en-US">Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Riszki Wijayatun Pratiwi, Gadjah Mada University, Master of Computer Science, Department of Computer Science</dc:contributor>
	<dc:date>2020-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/60733</dc:identifier>
	<dc:identifier>10.22146/ijccs.60733</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 4 (2020): October; 397-406</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/60733/29705</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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