Naive bayes algorithm performance for smartphone sentiment analysis in social media
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Poushter, J. (2016). Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center, 22, 1-44.
Steven, S. (2018). Consumer Dependence On Smart Phones: The Effect Of Social Needs, Social Influence And Convenience In Surabaya. Calyptra, 7(1), 839-857.
The shift in sales of smartphone brands in Indonesia is influenced by massive advertising carried out by smartphone vendors (smartphone capitalists) to consumers.
Jiang, L., Li, C., Wang, S., & Zhang, L. (2016). Deep feature weighting for naive Bayes and its application to text classification. Engineering Applications of Artificial Intelligence, 52, 26-39.
Wulandari, N., & Sari, R. K. (2016). Linking Experiential Value To Loyalty In Smartphone Industry. Studies And Scientific Researches. Economics Edition, (24).
Aggrawal, N., Ahluwalia, A., Khurana, P., & Arora, A. (2017). Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media. Social Network Analysis and Mining, 7(1), 21.
Esparza, G. G., Díaz, A. P., Canul-Reich, J., De-Luna, C. A., & Ponce, J. (2016). Proposal of a Sentiment Analysis Model in Tweets for improvement of the teaching-learning process in the classroom using a corpus of subjectivity. International Journal of Combinatorial Optimization Problems and Informatics, 7(2), 22-34.
Assemi, B., Safi, H., Mesbah, M., & Ferreira, L. (2016). Developing and validating a statistical model for travel mode identification on smartphones. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1920-1931.
Kumar, P., & Vardhan, M. (2018). Aspect-Based Sentiment Analysis of Tweets Using Independent Component Analysis (ICA) and Probabilistic Latent. Advances in Data and Information Sciences: Proceedings of ICDIS 2017, 2, 3.
Ginting, S. L. B., Adler, J., Ginting, Y. R., & Kurniadi, A. H. (2018, August). The Development of Bank Application for Debtors Selection by Using Naïve Bayes Classifier Technique. In IOP Conference Series: Materials Science and Engineering (Vol. 407, No. 1, p. 012177). IOP Publishing.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
Park, M. S. (2014). Configurable Accelerators for Visual and Text Analytics.
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press.
Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th international conference on machine learning (icml-03) (pp. 616-623).
Vijayarani, S., & Dhayanand, S. (2015). Liver disease prediction using SVM and Naïve Bayes algorithms. International Journal of Science, Engineering and Technology Research (IJSETR), 4(4), 816-820.
Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
Feitosa, S. A., Patel, D., Borges, A. L., Alshehri, E. Z., Bottino, M. A., Özcan, M., ... & Bottino, M. C. (2015). Effect of cleansing methods on saliva-contaminated Zirconia—An evaluation of resin bond durability. Operative dentistry, 40(2), 163-171.
Al-khurayji, R., & Sameh, A. (2017). An Effective Arabic Text Classification Approach Based on Kernel Naive Bayes Classifier. International Journal of Artificial Intelligence Applications, 01-10.
Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., & Zhang, C. (2015). Self-adaptive attribute weighting for Naive Bayes classification. Expert Systems with Applications, 42(3), 1487-1502.
Feng, W., Sun, J., Zhang, L., Cao, C., & Yang, Q. (2016, December). A support vector machine based naive Bayes algorithm for spam filtering. In Performance Computing and Communications Conference (IPCCC), 2016 IEEE 35th International (pp. 1-8). IEEE.
DOI: http://dx.doi.org/10.33292/ijarlit.v1i2.23
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