How to cite this paper
Shenify, M. (2024). Sentiment analysis of Saudi e-commerce using naïve bayes algorithm and support vector machine.International Journal of Data and Network Science, 8(3), 1607-1612.
Refrences
Afshoh, F., & Pamungkas, E.W. (2017). Analisa Sentimen Menggunakan Naïve Bayes Untuk Melihat Persepsi Masyarakat Terhadap Kenaikan Harga Jual Rokok Pada Media Sosial Twitter. Jurnal Muhammadiyah Surakarta, 1(1), 1-11.
Al-Barznji, K., & Atanassov, A. (2018, May). Big data sentiment analysis using machine learning algorithms. In Proceedings of 26th International Symposium" Control of Energy, Industrial and Ecological Systems, Bankia, Bulgaria.
Ardianto, R., Rivanie, T., Alkhalifi, Y., Nugraha, F. S., & Gata, W. (2020). Sentiment analysis on E-sports for education curriculum using naive Bayes and support vector machine. Jurnal Ilmu Komputer dan Informasi, 13(2), 109-122.
Arora, A., Patel, P., Shaikh, S., & Hatekar, A. (2020). Support vector machine versus naive bayes classifier: A juxtaposition of two machine learning algorithms for sentiment analysis. International Research Journal of Engineering and Technology, 7(7), 3553-3563.
Ayumi, V., & Fanany, M. I. (2016). A comparison of SVM and RVM for human action recognition. Internetworking Indonesia Journal, 8(1), 29-33. doi: 10.13140/RG.2.1.3986.0560.
Fikri, M.I., Sabrila, T.S., & Azhar, Y. (2020). Comparison of Naïve Bayes and Support Vector Machine Methods in Twitter Sentiment Analysis. SMATIKA Jurnal, 10(02), 71-6.
Frizka, F., Utami, E., & Al Fatta, H. (2021). Analysis of Opinion Sentiment towards the Covid-19 Vaccine on Twitter Social Media Using Support Vector Machine and Naïve Bayes. Komtika Journal, 5(1), 19-25.
Hakami, N. A. (2023). Identification of Customers Satisfaction with Popular Online Shopping Apps in Saudi Arabia Using Sentiment Analysis and Topic modelling. In Proceedings of the 2023 7th International Conference on E-Commerce, E-Business and E-Government (ICEEG '23). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3599609.3599610
Indriani, A. (2014). Classification of Forum Data using Naïve Bayes Classifier. Pp. 5–10 in National Seminar on Information Technology Application (SNATI).
Indriyani, E. R., & Wibowo, M. (2022). Comparison of the Naïve Bayes Method and Support Vector Machine for Sentiment Analysis towards the Astrazeneca Vaccine on Twitter. Media Informatika Budidarma Journal, 6(3), 1545-53.
Jurafsky, N. I. A., & Martin, J. H. (2009). Speech and Language Processing. 2nd Ed., Pearson Prentice Hall.
Luqyana, W. A., Cholissodin, I., & Perdana, R.S. (2018). Cyberbullying Sentiment Analysis in Instagram Comments Using the Support Vector Machine Classification Method. J-PTIIK Journal, Brawijaya University, 2(11), 4704–13.
Maarif, A. A. (2015). Application of the TF-IDF Algorithm for Searching for Scientific Works. B.Sc, Thesis, Dian Nuswantoro University, Semarang, Indonesia.
Petiwi, M.I., Triayudi, A., & Sholihati, I.D. (2022). Go-food Sentiment Analysis Based on Twitter Using Naïve Bayes Method and Support Vector Machine. Media Informatika Budidarma Journal, 6(1), 542-50.
Rama, G., Reddy, R., & Mamidi, R. (2015). Resource Creation Towards Automated Sentiment Analysis in Telugu ( a Low Resource Language ) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction. Proceedings of The Eleventh International Conference on Language Resources and Evaluation (LREC), European Language Resource Association (ELRA), Miyazaki, Japan. pp. 627-34.
Ramayanti, D., & Salamah, U. (2018). Complaint Classification Using Support Vector Machine for Indonesian Text Dataset. International Journal Science Resource Computational Science Engineering and Information Technology, 3(7), 179–84.
Saepulrohman, A., Saepudin, S., & Gustian, D. (2021). Analysis of WhatsApp Application User Satisfaction Sentiment Using the Naïve Bayes Algorithm and Support Vector Machine. The Best: Accounting Information Systems and Information Technology Business Enterprise, 6(2), 91-105.
Setiawan, H., & Utami, E. 2021). Post-Covid-19 Online Lecture Twitter Sentiment Analysis Using Support Vector Machine and Naïve Bayes Algorithms, Komtika Journal, 5(1), 43-51.
Sghaier, M. A., & Zrigui, M. (2016). Sentiment analysis for Arabic e-commerce websites. 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 2016, pp. 1-7, doi: 10.1109/ICEMIS.2016.7745323.
Supriyadi, R., Maulidah, N., Fauzi, A., Nalatissifa, H., & Diantika, S. (2022). Application of the Naive Bayes Algorithm and Support Vector Machine in Predicting Autism. SWABUMI, 10(1), 55-9.
Tamrakar, S., Bal, B. K., & Thapa, R. B. (2020). Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes. Technical Journal, 2(1), 22-29.
Tuhuteru, H., & Iriani, A. (2018). Sentiment Analysis of the Ambon Branch of the State Electric Company Using Support Vector Machine and Naive Bayes Classifier Methods. JIP-IT, 3(3), 394–401. doi: 10.30591/jpit.v3i3.977
Wang, N. Li, G., & Wang, Z. (2023). Fast SVM Classifier for Large-Scale Classification Problems, Information Sciences, 642.
Wardhani, N. K., Rezkiani, S. K., Setiawan, H. E. N. D. R. A., Gata, G. R. A. C. E., Tohari, S., Gata, W. I. N. D. U., & Wahyudi, M. O. C. H. A. M. A. D. (2018). Sentiment analysis article news coordinator minister of maritime affairs using algorithm naive bayes and support vector machine with particle swarm optimization. Journal of Theoretical and Applied InformationTechnology, 96(24), 8365-8378.
Al-Barznji, K., & Atanassov, A. (2018, May). Big data sentiment analysis using machine learning algorithms. In Proceedings of 26th International Symposium" Control of Energy, Industrial and Ecological Systems, Bankia, Bulgaria.
Ardianto, R., Rivanie, T., Alkhalifi, Y., Nugraha, F. S., & Gata, W. (2020). Sentiment analysis on E-sports for education curriculum using naive Bayes and support vector machine. Jurnal Ilmu Komputer dan Informasi, 13(2), 109-122.
Arora, A., Patel, P., Shaikh, S., & Hatekar, A. (2020). Support vector machine versus naive bayes classifier: A juxtaposition of two machine learning algorithms for sentiment analysis. International Research Journal of Engineering and Technology, 7(7), 3553-3563.
Ayumi, V., & Fanany, M. I. (2016). A comparison of SVM and RVM for human action recognition. Internetworking Indonesia Journal, 8(1), 29-33. doi: 10.13140/RG.2.1.3986.0560.
Fikri, M.I., Sabrila, T.S., & Azhar, Y. (2020). Comparison of Naïve Bayes and Support Vector Machine Methods in Twitter Sentiment Analysis. SMATIKA Jurnal, 10(02), 71-6.
Frizka, F., Utami, E., & Al Fatta, H. (2021). Analysis of Opinion Sentiment towards the Covid-19 Vaccine on Twitter Social Media Using Support Vector Machine and Naïve Bayes. Komtika Journal, 5(1), 19-25.
Hakami, N. A. (2023). Identification of Customers Satisfaction with Popular Online Shopping Apps in Saudi Arabia Using Sentiment Analysis and Topic modelling. In Proceedings of the 2023 7th International Conference on E-Commerce, E-Business and E-Government (ICEEG '23). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3599609.3599610
Indriani, A. (2014). Classification of Forum Data using Naïve Bayes Classifier. Pp. 5–10 in National Seminar on Information Technology Application (SNATI).
Indriyani, E. R., & Wibowo, M. (2022). Comparison of the Naïve Bayes Method and Support Vector Machine for Sentiment Analysis towards the Astrazeneca Vaccine on Twitter. Media Informatika Budidarma Journal, 6(3), 1545-53.
Jurafsky, N. I. A., & Martin, J. H. (2009). Speech and Language Processing. 2nd Ed., Pearson Prentice Hall.
Luqyana, W. A., Cholissodin, I., & Perdana, R.S. (2018). Cyberbullying Sentiment Analysis in Instagram Comments Using the Support Vector Machine Classification Method. J-PTIIK Journal, Brawijaya University, 2(11), 4704–13.
Maarif, A. A. (2015). Application of the TF-IDF Algorithm for Searching for Scientific Works. B.Sc, Thesis, Dian Nuswantoro University, Semarang, Indonesia.
Petiwi, M.I., Triayudi, A., & Sholihati, I.D. (2022). Go-food Sentiment Analysis Based on Twitter Using Naïve Bayes Method and Support Vector Machine. Media Informatika Budidarma Journal, 6(1), 542-50.
Rama, G., Reddy, R., & Mamidi, R. (2015). Resource Creation Towards Automated Sentiment Analysis in Telugu ( a Low Resource Language ) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction. Proceedings of The Eleventh International Conference on Language Resources and Evaluation (LREC), European Language Resource Association (ELRA), Miyazaki, Japan. pp. 627-34.
Ramayanti, D., & Salamah, U. (2018). Complaint Classification Using Support Vector Machine for Indonesian Text Dataset. International Journal Science Resource Computational Science Engineering and Information Technology, 3(7), 179–84.
Saepulrohman, A., Saepudin, S., & Gustian, D. (2021). Analysis of WhatsApp Application User Satisfaction Sentiment Using the Naïve Bayes Algorithm and Support Vector Machine. The Best: Accounting Information Systems and Information Technology Business Enterprise, 6(2), 91-105.
Setiawan, H., & Utami, E. 2021). Post-Covid-19 Online Lecture Twitter Sentiment Analysis Using Support Vector Machine and Naïve Bayes Algorithms, Komtika Journal, 5(1), 43-51.
Sghaier, M. A., & Zrigui, M. (2016). Sentiment analysis for Arabic e-commerce websites. 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 2016, pp. 1-7, doi: 10.1109/ICEMIS.2016.7745323.
Supriyadi, R., Maulidah, N., Fauzi, A., Nalatissifa, H., & Diantika, S. (2022). Application of the Naive Bayes Algorithm and Support Vector Machine in Predicting Autism. SWABUMI, 10(1), 55-9.
Tamrakar, S., Bal, B. K., & Thapa, R. B. (2020). Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes. Technical Journal, 2(1), 22-29.
Tuhuteru, H., & Iriani, A. (2018). Sentiment Analysis of the Ambon Branch of the State Electric Company Using Support Vector Machine and Naive Bayes Classifier Methods. JIP-IT, 3(3), 394–401. doi: 10.30591/jpit.v3i3.977
Wang, N. Li, G., & Wang, Z. (2023). Fast SVM Classifier for Large-Scale Classification Problems, Information Sciences, 642.
Wardhani, N. K., Rezkiani, S. K., Setiawan, H. E. N. D. R. A., Gata, G. R. A. C. E., Tohari, S., Gata, W. I. N. D. U., & Wahyudi, M. O. C. H. A. M. A. D. (2018). Sentiment analysis article news coordinator minister of maritime affairs using algorithm naive bayes and support vector machine with particle swarm optimization. Journal of Theoretical and Applied InformationTechnology, 96(24), 8365-8378.