Penerapan Metode Naïve Bayes Classifier Untuk Mendeteksi Emosi Pada Komentar Media Sosial

##plugins.themes.academic_pro.article.sidebar##

Published Feb 20, 2020
https://doi.org/10.51977/jti.v2i1.156
Download
PDF
Statistic

Downloads

Download data is not yet available.
Vol 2 No 1 (2020): Jurnal Responsif : Riset Sains dan Informatika

##plugins.themes.academic_pro.article.main##

Erfian Junianto
Universitas Adhirajasa Reswara Sanjaya
Rizal Rachman
STMIK Nusa Mandiri

Abstract

Keadaan psikologis yang dimiliki setiap orang diantaranya adalah emosi. Keadaan tersebut juga digunakan untuk membentuk hubungan atau menyampaikan pesan antara penulis dengan pembaca. Penetrasi internet yang semakin tinggi membuat gaya hidup masyarakat semakin berubah. Gaya hidup semakin terpengaruh oleh media sosial, sehingga membuat penyampaian informasi dan gagasan menjadi sangat bebas. Informasi dan gagasan tersebut selalu mengandung emosi dari penulisnya. Baik emosi marah, takut, bahagia, ataupun sedih. Pada penelitian ini akan diterapkan model text mining untuk melakukan deteksi melalui proses klasifikasi. Metode yang digunakan adalah naive bayes classifier. Sedangkan dataset yang digunakan sudah diberikan kelas oleh peneliti sebelumnya menggunakan best-worst scaling. Tantangan dari dataset ini adalah melakukan dari kalimat atau teks ke dalam 4 kategori emosi yaitu anger, fear, joy, dan sadness. Dari beberapa penelitian sebelumnya metode naive bayes classifier mampu menghasilkan akurasi yang cukup tinggi. Dengan diterapkan pada dataset emosi yang mempunyai karakter berbeda dari dataset kebanyakan, metode tersebut masih mampu memberikan kinerja yang baik dalam klasifikasi.


Kata Kunci:Text Mining, Klasifikasi, Deteksi Emosi, NaïveBayes Classifier

##plugins.themes.academic_pro.article.details##

Pemberitahuan Hak Cipta
Usulan kebijakan untuk jurnal yang menawarkan akses terbuka Syarat yang dibutuhkan penulis adalah sebagai berikut:

1. Penulis memegang hak cipta dan memberikan jurnal hak penerbitan pertama secara bersamaan di bawah lisensi di bawah Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0), yang mengizinkan orang lain untuk menyalin dan mendistribusikan ulang materi ini dalam bentuk atau format apa pun termasuk menyusun, memodifikasi, dan membuat turunan materi ini untuk tujuan apa pun, termasuk tujuan komersial.
2. Penulis dapat membuat pengaturan kontrak terpisah untuk distribusi non-eksklusif dari versi dekaden publikasi jurnal (misalnya mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan mengakui publikasi aslinya di jurnal ini.
3. Penulis diizinkan dan didorong untuk memposting artikel secara online (misalnya di repositori institusional atau di situs web mereka) seperti pada Garuda, Academia, researchgate atau repository lainnya sebelum dan selama proses pengiriman, karena dapat menghasilkan pertukaran yang produktif dan kutipan artikel yang diterbitkan lebih awal dan lebih maju (periksa: Efek Akses Terbuka)

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

References

Akaichi, J., Dhouioui, Z., & Lopez-Huertas Perez, M. J. (2013). Text mining facebook status updates for sentiment classification. 2013 17th International Conference on System Theory, Control and Computing, ICSTCC 2013; Joint Conference of SINTES 2013, SACCS 2013, SIMSIS 2013 - Proceedings, 640–645. https://doi.org/10.1109/ICSTCC.2013.6689032

Asy’arie, A. D., & Pribadi, A. W. (2009). Automatic news articles classification in Indonesian language by using Naive Bayes Classifier method. Proceedings of the 11th International Conference on Information Integration and Web-Based Applications & Services - IiWAS ’09, 658. https://doi.org/10.1145/1806338.1806463

Chukwuere, J. E. (2017). The impact of social media on social lifestyle : A case study of university female students. (December).

Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. (2016). A novel text mining approach based on TF-IDF and support vector machine for news classification. Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016, 112–116. https://doi.org/10.1109/ICETECH.2016.7569223

Durairaj, M., & Ramasamy, N. (2016). A comparison of the perceptive approaches for preprocessing the data set for predicting fertility success rate. International Journal of Control Theory and Applications, 9(27), 255–260. Retrieved from https://www.researchgate.net/publication/312225257

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., & Reyes, A. (2015). SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 470–478. https://doi.org/10.18653/v1/S15-2080

Hakim, A. A., Erwin, A., Eng, K. I., Galinium, M., & Muliady, W. (2015). Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. Proceedings - 2014 6th International Conference on Information Technology and Electrical Engineering: Leveraging Research and Technology Through University-Industry Collaboration, ICITEE 2014, 1–4. https://doi.org/10.1109/ICITEED.2014.7007894

Hamouda, S. Ben, & Akaichi, J. (2013). Social Networks’ Text Mining for Sentiment Classification : The case of Facebook’ statuses updates in the “Arabic Spring” Era. International Journal of Application or Innovation in Engineering & Management, 2(5), 470–478. Retrieved from http://www.ijaiem.org/Volume2Issue5/IJAIEM-2013-05-26-063.pdf

Hassan, S., Rafi, M., & Shaikh, M. S. (2011). Comparing SVM and Naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment. Proceedings of the 14th IEEE International Multitopic Conference 2011, INMIC 2011, 31–34. https://doi.org/10.1109/INMIC.2011.6151495

Izza, J. (2017). Infografis Penetrasi dan Perilaku Pengguna Internet Indonesia Tahun 2017. In Apjii. Retrieved from apjii.or.id

Jenkins, J. M., Oatley, K., & Keltner, D. (2013). Understanding Emotions, 3rd Edition (3rd ed.). Retrieved from http://bcs.wiley.com/he-bcs/Books?action=index&itemId=111814743X&bcsId=7957

Junianto, E., & Riana, D. (2017). Penerapan PSO Untuk Seleksi Fitur Pada Klasifikasi Dokumen Berita Menggunakan NBC. JURNAL INFORMATIKA, 4(1). Retrieved from https://s3.amazonaws.com/academia.edu.documents/52950408/INF-1810-4093-2-PB.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1558598523&Signature=8lawHoF6VvmzkxdUyFLe007DOhI%3D&response-content-disposition=inline%3B filename%3DPenerapan_PSO_Untuk_Seleksi_Fitur_Pada_K.pdf

Kamruzzaman, S. M., & Rahman, C. M. (2004). Text Categorization Using Association Rule and Naïve Bayes Classifier. Asian Journal of Information Technology (AJST). https://doi.org/10.3923/ajit.2004.657.665

Manning, C. D., Raghavan, P., & Schütze, H. (2009). IEEE Photonics Technology Letters information for authors. In IEEE Photonics Technology Letters (Vol. 21). https://doi.org/10.1109/LPT.2009.2020494

Mehmood, M. (2018). Essential Insights Into Internet, Social Media, Mobile And Ecommerce Use Around The World / Digital Information World. Retrieved August 29, 2018, from Digital Information World website: https://www.digitalinformationworld.com/2018/07/global-internet-stats-infographic.html

Mohammad, S. M., & Bravo-Marquez, F. (2017a). Emotion Intensities in Tweets. https://doi.org/10.18653/v1/S17-1007

Mohammad, S. M., & Bravo-Marquez, F. (2017b). WASSA-2017 Shared Task on Emotion Intensity. Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). https://doi.org/10.18653/v1/W17-5205

Powers, D. (2008). Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Mach. Learn. Technol., 2.

Rosenthal, S., Farra, N., & Nakov, P. (2017). SemEval-2017 Task 4: Sentiment Analysis in Twitter. Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), 502–518. Retrieved from http://aclweb.org/anthology/S/S17/S17-2088.pdf

Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S. M., Ritter, A., & Stoyanov, V. (2015). SemEval-2015 Task 10: Sentiment Analysis in Twitter. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 451–463. Retrieved from http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval078.pdf

Ruthven, I., & Lalmas, M. (2003). A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review, 18(2), S0269888903000638. https://doi.org/10.1017/S0269888903000638

Saif, H., & Fernandez, M. (2016). Contextual Semantics for Sentiment Analysis of Twitter Categories and Subject Descriptors. Elsevier, 52, 5–19. Retrieved from https://www.sciencedirect.com/science/article/pii/S0306457315000242

Santos, C. N. Dos, & Gattit, M. B. T.-I. C. on C. L. (2014). Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. Aclweb.Org. Retrieved from http://www.aclweb.org/anthology/C14-1008

Severyn, A., & Moschitti, A. (2015). Twitter Sentiment Analysis with Deep Convolutional Neural Networks. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’15, 959–962. https://doi.org/10.1145/2766462.2767830

Shen, Y., Li, S., Zheng, L., Ren, X., & Cheng, X. (2009). Emotion mining research on micro-blog. Proceedings - 2009 1st IEEE Symposium on Web Society, SWS 2009, 71–75. https://doi.org/10.1109/SWS.2009.5271711

Soelistio, Y. E., & Surendra, M. R. S. (2015). Simple Text Mining for Sentiment Analysis of Political Figure Using Naive Bayes Classifier Method. https://doi.org/10.12962/p9772338185001.a18

Wakade, S., Shekar, C., Liszka, K. J., & Chan, C.-C. (2012). Text mining for sentiment analysis of Twitter data. Proceedings of the International Conference on Information and Knowledge Engineering (IKE), 1. Retrieved from http://world-comp.org/p2012/IKE3997.pdf

Yam, C. . (2015). Emotion Detection and Recognition from Text Using Deep Learning - Developer Blog. Retrieved August 23, 2018, from Microsoft website: https://www.microsoft.com/developerblog/2015/11/29/emotion-detection-and-recognition-from-text-using-deep-learning/

Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial Management & Data Systems, 114(9), 1344–1359. https://doi.org/10.1108/IMDS-07-2014-0225

Zimbra, D., Ghiassi, M., & Lee, S. (2016). Brand-Related Twitter Sentiment Analysis Using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. 2016 49th Hawaii International Conference on System Sciences (HICSS), 1930–1938. https://doi.org/10.1109/HICSS.2016.244