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Open access Jurnal SINTA 2

Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 1 (2019) · pp. 53–62

DOI
10.22146/ijccs.41136
ISSN
2460-7258, 1978-1520
Published
2019-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.

Keywords

Collaboration network of the authors

36 authors · 61 co-authorship links · 13 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 PGRI Madiun
  • Politeknik Negeri Ketapang
  • Udayana University
  • Sebelas Maret University
  • National Central University
  • Muria Kudus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada5
Sri T. HartatiUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Agus HarjokoUniversitas Gadjah Mada2
Ilham UnggaraUniversitas Gadjah Mada2
Aditya PermanaUniversitas Gadjah Mada2
Timothy K. ShihNational Central University2
Sri AnardaniUniversitas PGRI Madiun1
Alim Citra Aria BimaAffiliation unknown1
Gilang PrimayogaAffiliation unknown1
Adriana Sari AryaniAffiliation unknown1
Fajar Ibnu TufeilAffiliation unknown1
Mujiono MujionoAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Ria AstriratmaUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Damar RiyadiUniversitas Gadjah Mada1
Dias Aziz PramuditaUniversitas Gadjah Mada1
Abidah ElcholiqiUniversitas Gadjah Mada1
Ida Bagus Gede SarasvanandaUdayana University1
Wahyu SrimulyaniUniversitas Gadjah Mada1
Alfian Abdul JalidUniversitas Gadjah Mada1
Hans Satria KusumaUniversitas Gadjah Mada1
Yunita SariUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Faisal RamadhanUniversitas Gadjah Mada1
Muhammad Bintang BahyUniversitas Gadjah Mada1
Astrid Noviana ParadhitaSebelas Maret University1
Agus SihabuddinUniversitas Gadjah Mada1
Muhammad Fawwaz MaydaUniversitas Gadjah Mada1
Anastasya LatubessyMuria Kudus University1
Sri KusrohmaniahUniversitas 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">Sarcasm Detection For Sentiment Analysis in Indonesian Tweets</dc:title>
	<dc:creator>Yunitasari, Yessi</dc:creator>
	<dc:creator>Musdholifah, Aina</dc:creator>
	<dc:creator>Sari, Anny Kartika</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Naïve bayes;sarcasm;tweet;sentiment analysis;random forest</dc:subject>
	<dc:description xml:lang="en-US">Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-01-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/41136</dc:identifier>
	<dc:identifier>10.22146/ijccs.41136</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 1 (2019): January; 53-62</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/41136/23719</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 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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