Sentiment Analysis on Twitter Social Media Regarding Depression Disorder Using the Naive Bayes Method

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Published: Jul 30, 2023
DOI: https://doi.org/10.60005/coreid.v1i2.14
Keywords:
Depression Disorder Twitter Naive Bayes Classification

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Nur Lickha Lavenia
Information System, UPN “Veteran” Jawa Timur
Reisa Permatasari
Information System, Computer Science Faculty, UPN Veteran Jawa Timur

Abstract

Depression disorder is a serious issue in mental health that affects many individuals worldwide. This research analyzes the sentiments related to depression disorder on Twitter using the Naïve Bayes method. Depression-related tweet data was collected through snscrape and processed to eliminate irrelevant information. Three Naïve Bayes methods, namely Multinomial, Gaussian, and Bernoulli, were compared to classify positive, negative, or neutral sentiments in each tweet. The results of the study indicate that Multinomial Naïve Bayes exhibited the best performance with an accuracy rate of 90.13%, followed by Gaussian Naïve Bayes (88.38%), and Bernoulli Naïve Bayes (85.37%).

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How to Cite
[1]
N. L. Lavenia and R. Permatasari, “Sentiment Analysis on Twitter Social Media Regarding Depression Disorder Using the Naive Bayes Method”, coreid, vol. 1, no. 2, pp. 66–74, Jul. 2023.
Issue
Vol. 1 No. 2 (2023): July 2023
Section
Articles
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References

Basuma, D. (2013). Pencarian Alamat Fasilitas Umum Menggunakan Metode Vector Space Model (Studi Kasus Kota Pekanbaru) (Doctoral dissertation, UNIVERSITAS ISLAM NEGERI SULTAN SYARIEF KASIM RIAU).

Budiman, K., Zaatsiyah, N., Niswah, U., & Faizi, F. M. N. (2020). Analysis of sexual harassment tweet sentiment on twitter in Indonesia using naïve Bayes method through national institute of standard and technology digital forensic acquisition approach. Journal of Advances in Information Systems and Technology, 2(2), 21-30.

Chandra, D. N., Indrawan, G., & Sukaraja, I. N. (2016). Klasifikasi Berita Lokal Radar Malang Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram. Jurnal Ilmiah Teknologi Informasi Asia, 10(1), 11-19.

Fahrudin, T. M., Ruhui, A., Sari, F., Iffadah, A.Priambodo, J. (2018). Pendeteksian plagiarisme menggunakan algoritma Rabin-Karp dengan metode Rolling Hash. Jurnal Informatika Universitas Pamulang, 3(1), 39-45. S., Windyadari, C. C., & Khusnul, G. (2022). Pemodelan Teks Tweet pada Isu Pelecehan Seksual Berbasis Analisis Sentimen dan Leksikon Emosi. 2022(Senada), 12–23..

Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press.Ratnawati, F. (2018). Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter. INOVTEK Polbeng-Seri Informatika, 3(1), 50-59

A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “A decentralized privacy-preserving healthcare blockchain for IoT,” Sensors (Switzerland), vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.

Hakimi, F. D. D. (2018). Sistem analisis sentimen publik tentang opini pemilihan Kepala Daerah Jawa Timur 2018 pada dokumen twitter menggunakan naive bayes classifier (Doctoral dissertation, UIN Sunan Ampel Surabaya).

Hidayatullah, M., Alam, S., & Jaelani, I. (2021). Sentiment Analysis of Police Performance On Twitter Users Using Naïve Bayes Method. RISTEC: Research in Information Systems and Technology, 2(2), 29-40.

Hidayatullah, A. F., & Azhari, A. S. (2015, July). Analisis sentimen dan klasifikasi kategori terhadap tokoh publik pada twitter. In Seminar Nasional Informatika (SEMNASIF) (Vol. 1, No. 1).

Indrayuni, E., & Wahyudi, M. (2015). Penerapan character N-gram untuk sentiment analysis review hotel menggunakan algoritma naive bayes. Konferensi Nasional Ilmu Pengetahuan dan Teknologi, 1(1), 83-88.

Krisnanto, F., Tristiyanto, T., & Ardiansyah, A. (2018). Simulasi Sistem Informasi Komoditas Pasar Berbasis Web Menggunakan Metode Continuous Double Auction. Jurnal Komputasi, 6(2), 88-96.

Lestari, A. R. T., Perdana, R. S., & Fauzi, M. A. (2017). Analisis sentimen tentang opini pilkada dki 2017 pada dokumen twitter berbahasa indonesia menggunakan naive bayes dan pembobotan emoji. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X

Madani, S. A., Kazmi, J., & Mahlknecht, S. (2010). Wireless sensor networks: modeling and simulation. Discret. Event Simulations, (2004), 1-16.

Mahardhika, Y. S., & Zuliarso, E. (2018). Analisis Sentimen Terhadap Pemerintahan Joko Widodo Pada Media Sosial Twitter Menggunakan Algoritma Naives Bayes Classifier.

Manning, C. D. (2008). Introduction to information retrieval. Syngress Publishing,.

Nurhuda, F., Sihwi, S. W., & Doewes, A. (2016). Analisis sentimen masyarakat terhadap calon Presiden Indonesia 2014 berdasarkan opini dari Twitter menggunakan metode Naive Bayes Classifier. ITSmart: Jurnal Teknologi dan Informasi, 2(2), 35-42..

Novantirani, A., Sabariah, M. K., & Effendy, V. (2015). Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine. eProceedings of Engineering, 2(1).

Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, No. 2010, pp. 1320-1326).

Priambodo, J. (2018). Pendeteksian plagiarisme menggunakan algoritma Rabin-Karp dengan metode Rolling Hash. Jurnal Informatika Universitas Pamulang, 3(1), 39-45.

Ratnawati, F. (2018). Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter. INOVTEK Polbeng-Seri Informatika, 3(1), 50-59.

Rini, D. C., Farida, Y., & Puspitasari, D. (2016). Klasifikasi menggunakan metode hybrid bayessian-neural network: studi kasus identifikasi virus komputer. Jurnal Matematika MANTIK, 1(2), 38-43.

M. Aqib, R. Mehmood, A. Alzahrani, I. Katib, A. Albeshri, and S. M. Altowaijri, Smarter traffic prediction using big data, in-memory computing, deep learning and gpus, vol. 19, no. 9. 2019.

Samsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. Jurnal Media Informatika Budidarma, 5(1), 157-163.

Subari, & Ferdinandus. (2015). Sistem Information Retrieval Layanan Kesehatan Untuk Berobat Dengan Metode Vector Space Model (Vsm) Berbasis Webgis. Snatika2, 3(November), 202–212.

Tripathi, G., & Naganna, S. (2015). Feature selection and classification approach for sentiment analysis. Machine Learning and Applications: An International Journal, 2(2), 1-16.