Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)

Agus Sasmito Aribowo, Siti Khomsah

Abstract


Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion from time to time are interesting to analyze, especially about the patterns of public sentiment and emotions contained in these opinions. Sentiment and emotional conditions can illustrate the public's response to the Covid-19 pandemic in Indonesia. This research has two objectives, first to reveal the types of public emotions that emerged during the Covid-19 pandemic in Indonesia. Second, reveal the topics or words that appear most frequently in each emotion class. There are seven types of emotions to be detected, namely anger, fear, disgust, sadness, surprise, joy, and trust. The dataset used is Indonesian-language tweets, which were downloaded from April to August 2020. The method used for the extraction of emotional features is the lexicon-based method using the EmoLex dictionary. The result obtained is a monthly graph of public emotional conditions related to the Covid-19 pandemic in the dataset.


Keywords


Covid-19;Emotion Analysis; Lexicon

Full Text:

PDF

References


M. Lailiyah, S. Sumpeno, and I. K. E. Purnama, “Sentiment analysis of public complaints using lexical resources between Indonesian sentiment lexicon and sentiwordnet,” in 2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding, 2017, vol. 2017-Janua, pp. 307–312, doi: 10.1109/ISITIA.2017.8124100.

Y. Fauziah, S. Saifullah, and A. S. Aribowo, “Design Text Mining for Anxiety Detection using Machine Learning based-on Social Media Data during COVID-19 pandemic,” Proceeding LPPM UPN “Veteran” Yogyakarta Conf. Ser. 2020–Engineering Sci. Ser., vol. 1, no. 1, pp. 253–261, 2020, doi: 10.31098/ess.v1i1.117.

B. Liu, Sentiment Analysis and Opinion Mining. 2012.

S. Mohammad, “Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text,” Emot. Meas., pp. 201–237, 2021, doi: 10.1016/B978-0-08-100508-8.00009-6.

P. Ekman, “An argument for basic emotions An Argument for Basic Emotions,” no. October 2014, pp. 37–41, 2008, doi: 10.1080/02699939208411068.

A. S. Aribowo, H. Basiron, N. S. Herman, and S. Khomsah, “Fanaticism Category Generation Using Tree-Based Machine Learning Method Fanaticism Category Generation Using Tree-Based Machine Learning Method,” J. Phys. Conf. Ser., 2020, doi: 10.1088/1742-6596/1501/1/012021.

S. M. Mohammad and P. D. Turney, “Crowdsourcing a Word–Emotion Association Lexicon,” no. 2010, pp. 1–25, 2013.

S. M. Mohammad, “Sentiment and Emotion Lexicons.”

B. Agarwal, N. Mittal, P. Bansal, and S. Garg, “Sentiment analysis using common-sense and context information,” Comput. Intell. Neurosci., vol. 2015, 2015, doi: 10.1155/2015/715730.

N. D. Gitari, Z. Zuping, H. Damien, and J. Long, “A Lexicon-based Approach for Hate Speech Detection,” Int. J. Multimed. Ubiquitous Eng., vol. 10, no. 4, pp. 215–230, 2015, doi: 10.14257/ijmue.2015.10.4.21.

V. Balakrishnan, M. C. Martin, W. Kaur, and A. Javed, “A comparative analysis of detection mechanisms for emotion detection,” 2019, doi: 10.1088/1742-6596/1339/1/012016.

A. N. Rohman, E. Utami, and S. Raharjo, “Deteksi Emosi Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing,” pp. 70–76, 2019, doi: 10.30864/eksplora.v9i1.277.

“https://developer.twitter.com/en/docs.”

S. Mujilahwati, “Pre-Processing Text Mining Pada Data Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2016, no. Sentika, pp. 2089–9815, 2016.

S. Khomsah and A. S. Aribowo, “Model Text-Preprocessing Komentar Youtube Dalam Bahasa Indonesia,” Rekayasa Sist. dan Teknol. Informasi, RESTI, vol. 4, no. 10, pp. 648–654, 2020, doi: https://doi.org/10.29207/resti.v4i4.2035.

A. S. Aribowo, “Arsitektur Aplikasi Twitter Opinion Mining Untuk Mengetahui Sentimen Publik Terhadap Merek,” in Seminar Nasional Informatika (semnasIF) 2015 UPN Veteran Yogyakarta, 2015, vol. 2015, no. November, pp. 14–20.




DOI: https://doi.org/10.31315/telematika.v18i1.4341

DOI (PDF): https://doi.org/10.31315/telematika.v18i1.4341.g3345

Refbacks

  • There are currently no refbacks.


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

Copyright of :
TELEMATIKA: Jurnal Informatika dan Teknologi Informasi
ISSN 1829-667X (print); ISSN 2460-9021 (online)


Dipublikasi oleh
Jurusan Teknik Informatika, UPN Veteran Yogyakarta
Jl. Babarsari 2 Yogyakarta 55281 (Kampus Unit II)
Telp: +62 274 485786
email: jurnaltelematika@upnyk.ac.id

 

Jurnal Telematika sudah diindeks oleh beberapa lembaga berikut:
 

 

 

 

 

Status Kunjungan Jurnal Telematika
slot gacor slot