Electronic Resource

Article - Argument Identification in Indonesian Tweets on the Issue of Moving the Indonesian Capital ( Volume 191, Halaman 128–135)

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Last October 2019, Indonesian Twitter community is busy discussing the issue of moving the capital city, and people are very
eager to share their opinion in various expressions. This form of expression was alleged as a form of society expressing their
opinions and arguments. This research uses a dataset from online discussions about moving Indonesian capital on Twitter. The
goal of this study aims to identify whether a tweet contains argument or not. In this experiment, we use Multi-Class Support
Vector Machine (SVM), and Multinomial Naïve Bayes (MNB) as the classifier and TF-IDF as feature extraction. Variation of
Twitter data characters that have a lot of noise will be a challenge in this study so that some preprocessing processes will be
carried out to overcome this problem. This research will investigate several combinations of preprocessing to discover the best
result. We classify each tweet information such as argument, non-argument, and unknown. The best results with an accuracy of
71.42% were obtained by performing SVM with only a unigram feature. This study shows that the stopwords feature has
effectiveness depends on which feature combination is implemented in the model.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

006.35 HUW a

Penerbit

: Elsevier B.V.., 2021

Deskripsi Fisik

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Bahasa

English

ISBN/ISSN

1877-0509

Klasifikasi

006.35

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Volume 191, Halaman 128–135

Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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