Sentiment Analysis of Online Lectures Tweets using Naïve Bayes Classifier
DOI:
https://doi.org/10.31154/cogito.v8i2.414.371-384Keywords:
Confusion Matrix, Twint, Twitter, Text Preprocessing, Covid-19 pandemicAbstract
Online lecture is an alternative learning method during the Covid-19 pandemic. There are opinions with pro and contra of the learning method. The purpose of this study is to evaluate the tweets of opinion or sentiment retrieved from social media Twitter regarding online lectures among the Indonesian community. Twint is used to collect the data tweet and Jupyter notebook is for text preprocessing and classification. The processes started with scraping data from Twitter, text preprocessing, and text classification. Using the Naïve Bayes classifier shows the performance has a precision value of 100%, an accuracy value of 70.8%, an F-measure of 10.2%, and a recall value of 5.4%. Performance rating can be affected by the dataset used for modeling. This analysis covers the positive sentiment and negative sentiments toward online lectures and the result shows 69% negative sentiments and 31% positive sentiments. The negative sentiments had a higher percentage compared to positive sentiments. The results were also supported by the word cloud which expressed a high frequency of negative words such as sleep problems, bored, tired, dizzy, difficult and lazy. So, it is concluded that during the Covid-19 pandemic from August 1, 2020, to May 31, 2021, Twitter users in Indonesia had negative sentiments about online lectures.References
Setyorini, I., 2020, Pandemi COVID-19 dan online learning: apakah berpengaruh terhadap proses pembelajaran pada kurikulum 13?), Journal of Industrial Engineering & Management Research, vol. 1, no. 1, pp. 95. [Online]. Available: https://tinyurl.com/3v4kmuku
Anhusadar, L., 2020, “PIAUD student perceptions of online lectures in Covid 19 pandemic period (Persepsi Mahasiswa PIAUD terhadap Kuliah Online di Masa Pandemi Covid 19),” Kindergarten Journal of Islamic Early Childhood Education, vol. 3, no. 1, pp. 45. [Online]. Available: https://tinyurl.com/yc5ky87x
Kusnayat, A., Muiz, M.H., Sumarni, N., Mansyur A.S., and Zaqiah, Q.Y., 2020, Pengaruh teknologi pembelajaran kuliah online di era Covid-19 dan dampaknya terhadap mental mahasiswa, EduTeach, vol. 1, no. 2, pp. 153–165. [Online]. Available: https://tinyurl.com/35dbbcsk
Mulawarman, W.G., 2020, Persoalan dosen dan mahasiswa masa pandemik Covid 19: dari gagap teknologi hingga mengeluh boros paket data), Prosiding Seminar Nasional Hardiknas, vol. 1, pp. 37–46. [Online]. Available: https://tinyurl.com/2p8pcjeh
Sulis, M., 2020, Resistance to change mahasiswa psikologi universitas Esa Unggul yang mengikuti perkuliahan online, UEU Journal, vol. 18, no. 02, pp. 87. [Online]. Available: https://tinyurl.com/5687bz72
Zhang, Y., Chen. F., and Rohe, K., 2021, Social media public opinion as flock in a murmuration: conceptualizing and measuring opinion expression on social media, Journal of Computer-Mediated Communication, vol. 27, issue 1, pp. 1-22. [Online]. Available: https://academic.oup.com/jcmc/article/27/1/zmab021/6472790
Gil, P., 2021, What is Twitter & how does it work?, Lifewire. [Online]. Available: https://www.lifewire.com/what-exactly-is-twitter-2483331.
Samsir., Ambiyar., Verawardina, U., Edi, F., and Watriantros, R., 2021, Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes, Jurnal Media Informatika Budidarma, vol. 5, no. 1, pp 157-163. [Online]. Available: http://stmik-budidarma.ac.id/ejurnal/index.php/mib/article/view/2580
Suharsa, H., Soleh, D.J., and Miftahuddin, A., 2022, Persepsi Publik tentang Pembelajaran Daring dari Jejak Digital Twitter: Analisis Sentiment Positif, Netral, dan Negativ dari Drone Imprit, Jurnal Aparatur Kementrian Energi dan Sumber Daya Mineral, vol. 6, no. 1, pp. 33-42. [Online]. Available: https://tinyurl.com/w44ys72v
Razaq, E.R.M., Jacob, D.W., and Hamami, F., 2021, Analisis Sentimen Kepuasan Mahasiswa terhadap Pembelajaran Online selama Pandemi Covid-19 pada Media Sosial Twitter menggunakan Perbandingan Algoritma Klasifikasi, e-Proceeding of Engineering¸ vol. 8, no. 5. [Online]. Available: https://tinyurl.com/34swtsvf
Nursyi’ah, S.Y., Erfina, A., and Warman, C., 2021, Analisis Sentimen Pembelajaran Daring pada Masa pandemi Covid-19 di Twitter menggunakan Naïve Bayes, Sismatik (Seminar Nasional Sistem Informasi dan Manajemen Informatika), vol. 1, no.1. pp 117-123. [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/16
Musfiroh, D., Khaira, U., Utomo, P.E.P., and Suratno, T., 2021, Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset menggunakan InSet Lexicon, MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 1, no.1, pp. 24-33. [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/view/20
Hardian, R.Z., Prasetyo, P.E., Khaira, U., and Suratno, T., 2021, Analisis Sentiment Kuliah Daring di Media Sosial Twitter selama Pandemi Covid-19 menggunakan Algoritma Sentistrength, MALCOM: Indonesian Journal of Machine Learning and Computer Science vol. 1, issue. 2 pp. 138-143. [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/view/15
Sahir, S.H., Ramadhana, R.S.A., Marpaung, M.F.R., Munthe, S.R., Watrianthos, R., 2021, Online Learning Sentiment Analysis During the Covid-19 Indonesia Pandemic Using Twitter Data, IOP Conf. Ser.: Mater. Sci. Eng, vol 1156. [Online]. Available: https://iopscience.iop.org/article/10.1088/1757-899X/1156/1/012011/meta
Dhaduk, H., 2021, Performing Sentiment Analysis With Naive Bayes Classifier!, Data Science Blogathon. [Online]. Available: https://tinyurl.com/243d8nrh
Sari, F. V., and Wibowo, A., 2019, Analisis sentimen pelanggan toko online JD.ID menggunakan metode naïve bayes classifier berbasis konversi ikon emosi, Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 10, no. 2, pp. 681-686. [Online]. Available: https://jurnal.umk.ac.id/index.php/simet/article/view/3487
Indurkhya, N., and Damerau, F. J., 2010, Handbook of Natural Language Processing. CRC Press. [E-book] Available: Google Books.
Arviana, G.N., 2020, Sentiment Analysis: Pengertian, Teknik, dan Penggunaannya, Glints Blog. [Online]. Available: https://glints.com/id/lowongan/sentiment-analysis/ [Accessed Mar 16, 2021].
Rao, P., 2019, Fine-grained Sentiment Analysis in Python (Part 1), Medium. [Online]. Available: https://towardsdatascience.com/fine-grained-sentiment-analysis-in-python-part-1-2697bb111ed4 [Accessed Mar 16, 2021].
Gaind, B., Syal, V., and Padgalwar, S., 2019, Emotion Detection and Analysis on Social Media. [Online]. Available: http://arxiv.org/abs/1901.08458. [Accessed Mar. 16, 2021]
Aldhi, M. D., 2016, Aspect-Based Sentiment Analysis terhadap Ulasan Produk menggunakan Metode Klasifikasi Naïve Bayes, Universitas Telkom.
Damayanti, W., 2015, Analisis penggunaan multilingual anak tingkat sekolah dasar di lingkungan gang siti mardiah cibaduyut bandung, studi sosiolinguistik), Jurnal Gramatika, vol. 1, no. 1, pp.100-110, Apr. 2015. [Online]. Available: https://tinyurl.com/2p94sazs
Putri, W. S. R., Nurwati, N., and Budiarti S. M., 2016, Pengaruh media sosial terhadap perilaku remaja), Prosiding KS, vol. 3, no. 1, pp.47-51. [Online]. Available: https://jurnal.unpad.ac.id/prosiding/article/view/13625
Oktaviani, D., 2019., Pengaruh Media Sosial Terhadap Gaya Hidup Mahasiswa IAIN Metro,” Thesis (undergraduate), IAIN Metro, 2019. [Online]. Available: https://repository.metrouniv.ac.id/id/eprint/1212/
Ambar, 2017, 20 Pengertian Media Sosial Menurut Para Ahli, Jun. 08, 2017. [Online]. Available: https://tinyurl.com/5n8y6ty8. [Accessed Mar. 18, 2021].
Sembodo, J. E., Setiawan, E. B., and Baizal, Z. A., 2016, Data Crawling Otomatis pada Twitter, in INDOSC 2016, pp. 11–16.
O’Reilly, T., and Milstein, S., 2011, The Twitter Book. O’Reilly Media, Inc. [E-book] Available: Google Books.
Kusnandar, V. B., 2019, Twitter, Aplikasi Berita dan Majalah Berbasis Android dengan Rating Tertinggi), Databoks. [Online]. Available: https://tinyurl.com/mrxa3cj6 [Accessed Mar. 14, 2021].
Jayani, D. H., 2020, Databoks. [Online]. Available: https://tinyurl.com/t5d8v72w [Accessed Mar. 14, 2021].
Rahutomo, F., Saputra, P. Y., and Fidyawan, M. A., 2018, Implementasi Twitter sentiment analysis untuk review film menggunakan algoritma support vector machine),” JIP, vol. 4, no. 2, pp. 93, Feb. 2018.
Monarizqa, N., Nugroho, L. E., and Hantono, B. S., 2014, Penerapan analisis sentimen pada Twitter berbahasa indonesia sebagai pemberi rating, UGM, vol.1, no.4, pp. 5, Oct. 2014. [Online]. Available: httphttps://tinyurl.com/3b3azywf
Saputro, F. B., Somantri, M., and Nugroho, A., 2017, Pengembangan sistem kuliah online Universitas Diponegoro untuk antar muka mahasiswa pada perangkat bergerak berbasis Android,” Transmisi, vol. 19, no. 1, pp. 1-2, Jul. 2017. [Online]. Available: https://tinyurl.com/drabs2d7
Erin, E., and Maharani, A, 2018, Persepsi Mahasiswa Pendidikan Matematika terhadap Perkuliahan Online),” Mosharafa, vol. 7, no. 3, pp. 337–344.
Palmer, D.D., 2010, Text Preprocessing. Handbook of natural language processing 2. [E-book] Available: Google Scholar.
Dashtipour K., et al., 2016, Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques, Cognitive Computation, vol. 8, no. 7, pp. 757-771.
Fatra, A.H.D., Hayatin, N. H., and Aditya, C.S.K., 2020, Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia, Jurnal Repositor, vol. 2, no.5, pp. 977-984.
Chakrabarti, S., Roy, S., and Soundalgekar, M. V., 2003, Fast and accurate text classification via multiple linear discriminant projections, VLDB, vol. 12, no. 2, pp. 170–185, Aug. 2003.
Domingos, P., and Pazzani, M., 1996, Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier, Proc. 13th Intl. Conf. Machine Learning, pp. 105-112
Susilowati, E., Sabariah, M. K., and Gozali, A. A., 2015, Implementasi metode Support Vector Machine untuk melakukan klasifikasi kemacetan lalu lintas pada Twitter, e-Proceeding of Engineering, vol. 2, no. 1, pp. 1-7.
Olhang, M., Achmadi, S., and Wibisono, F. X. A., 2020, Analisis sentimen pengguna Twitter terhadap Covid-19 di Indonesia menggunakan metode Naïve Bayes classifier (nbc), Jati, vol. 4, no. 2, pp. 214–221, Sep. 2020. [Online]. Available: https://ejournal.itn.ac.id/index.php/jati/article/view/2695
Najjichah, H., Syukur, A., and Subagyo, H., 2019, Pengaruh text preprocessing dan kombinasinya pada peringkas dokumen otomatis teks berbahasa Indonesia, Jurnal Teknologi Informasi, Jurnal Teknologi Informasi Cyberku, vol. 15, p. 1-11. [Online]. Available: http://research.pps.dinus.ac.id/index.php/Cyberku/article/view/69
Nurvinda, G., 2021, Pentingnya Preprocessing dalam Pengolahan Data Statistik, DQLab. [Online]. Available: https://tinyurl.com/32nkdfp9 [Accessed Juni 22, 2021].
Januarsjaf, A., 2020, Wordcloud, RPubs. [Online]. Available: https://tinyurl.com/4sbnzexj. [Accessed Juni 30, 2021].
Steinbock, D., Create your own word cloud from any text to visualize word frequency, TagCrowd. [Online]. Available: https://tagcrowd.com/. [Accessed Juni 30, 2021].
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 CogITo Smart Journal

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).