Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM

 Chairullah Naury (Universitas Islam Indonesia, Yogyakarta, Indonesia)
 (*)Dhomas Hatta Fudholi Mail (Universitas Islam Indonesia, Yogyakarta, Indonesia)
 Ahmad Fathan Hidayatullah (Universitas Islam Indonesia, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: October 28, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2556

Abstract

The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.

Keywords


Sentiment Analysis; Long Short-Term Memory; Online News Headline; Topic Modelling; Latent Dirichlet Allocation

Full Text:

PDF


Article Metrics

Abstract View: 122 times | PDF View: 35 times

References

Badan Pengembangan Bahasa dan Perbukuan, “Kamus Besar Bahasa Indonesia,” Kementerian Pendidikan dan Kebudayaan, 2016. https://kbbi.kemdikbud.go.id/.

M. Wandik, A. M.Golung, and H.Mulyono, “Proses Penentuan Headline Surat Kabar (Studi Pada Surat Kabar Harian Manado Post),” E-Journal Acta Diurna, vol. VI, no. 2, 2017.

M. Winiharti, “Analisis Diksi Pada Judul Berita Utama Surat Kabar yang Memberitakan Rapat Pansus DPR RI untuk Kasus Bank Century,” J. Penelit. Hum., vol. 12, no. 1, pp. 19–31, 2011.

C. C. Aggarwal and C. X. Zhai, Mining text data, vol. 9781461432. 2013.

C. Jacobi, W. Van Atteveldt, and K. Welbers, “Quantitative analysis of large amounts of journalistic texts using topic modelling,” Digit. Journal., vol. 4, no. 1, pp. 89–106, 2016, doi: 10.1080/21670811.2015.1093271.

S. Bergamaschi and L. Po, “Comparing LDA and LSA topic models for content-based movie recommendation systems,” Lect. Notes Bus. Inf. Process., vol. 226, pp. 247–263, 2015, doi: 10.1007/978-3-319-27030-2_16.

Y. Lu, Q. Mei, and C. X. Zhai, “Investigating task performance of probabilistic topic models: An empirical study of PLSA and LDA,” Inf. Retr. Boston., vol. 14, no. 2, pp. 178–203, 2011, doi: 10.1007/s10791-010-9141-9.

D. M. Blei, B. B. Edu, A. Y. Ng, A. S. Edu, M. I. Jordan, and J. B. Edu, “Latent Dirichlet Allocation,” CrossRef List. Deleted DOIs, vol. 1, pp. 993–1022, 2003, doi: 10.1162/jmlr.2003.3.4-5.993.

L. Hagen, “Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models?,” Inf. Process. Manag., vol. 54, no. 6, pp. 1292–1307, 2018, doi: 10.1016/j.ipm.2018.05.006.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014, doi: 10.1016/j.asej.2014.04.011.

K. Ivanedra and M. Mustikasari, “Implementasi Metode Recurrent Neural Network Pada Text Summarization Dengan Teknik Abstraktif the Implementation of Text Summarization With Abstractive Techniques Using Recurrent Neural Network Method,” vol. 6, no. 4, pp. 377–382, 2019, doi: 10.25126/jtiik.201961067.

R. Primartha, Belajar Machine Learning Teori dan Praktik. Bandung: Penerbit INFORMATIKA, 2018.

B. K. Wangsa, D. Utomo, and S. Nugroho, “Sistem Peringkas Berita Otomatis berbasis Text Mining menggunakan Generalized Vector Space Model: Studi Kasus Berita diambil dari Media Massa Online,” Techne J. Ilm. Elektrotek., vol. 13, pp. 231–241, 2014.

M. R. Pratiwi, “Peran ICT bagi Organisasi Media Massa dan Budaya Masyarakat,” Komunikator, vol. 6, no. 5, p. 21, 2014, [Online]. Available: http://journal.umy.ac.id/index.php/jkm/article/view/212/174.

Y. Yuniati, “Pengaruh Berita di Surat Kabar terhadap Persepsi Mahasiswa tentang Politik,” vol. 3, no. 1. pp. 1–17, 2002.

F. de Oliveira Capela and J. E. Ramirez-Marquez, “Detecting urban identity perception via newspaper topic modeling,” Cities, vol. 93, no. May, pp. 72–83, 2019, doi: 10.1016/j.cities.2019.04.009.

X. Li, P. Wu, and W. Wang, “Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong,” Inf. Process. Manag., vol. 57, no. 5, p. 102212, 2020, doi: 10.1016/j.ipm.2020.102212.

C. C. Lai, T. P. Shih, W. C. Ko, H. J. Tang, and P. R. Hsueh, “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges,” Int. J. Antimicrob. Agents, vol. 55, no. 3, p. 105924, 2020, doi: 10.1016/j.ijantimicag.2020.105924.

WHO, “Coronavirus disease 2019,” World Heal. Organ., vol. 2019, no. March, p. 2633, 2020, doi: 10.1001/jama.2020.2633.

N. Reimers and I. Gurevych, “Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks,” 2017, [Online]. Available: http://arxiv.org/abs/1707.06799.

A. F. Hidayatullah, A. M. Hakim, S. Cahyaningtyas, and W. P. Aulia, “TOPIC MODELING DATA TWITTER TERHADAP CALON PRESIDEN REPUBLIK INDONESIA 2019 MENGGUNAKAN METODE LATENT DIRICHLET ALLOCATION (LDA).”

A. F. Hidayatullah, E. C. Pembrani, W. Kurniawan, G. Akbar, and R. Pranata, “Twitter Topic Modeling on Football News,” 2018 3rd Int. Conf. Comput. Commun. Syst. ICCCS 2018, pp. 94–98, 2018, doi: 10.1109/CCOMS.2018.8463231.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.