Naive bayes algorithm performance for smartphone sentiment analysis in social media

Monalisa Fatmawati Sarifah(1*),

(*) Corresponding Author


Indonesia with a population of 250 million is a large market, Millennials tend to be more adaptive to the development of communication technology [1]. There are lot of opportunities that are used by various groups, one of which is the need to use smartphones that can make it easier for people to exchange information [2].  The shift in sales of smartphone brands in Indonesia is influenced by  massive advertising carried out by smartphone vendors (smartphone capitalists) to consumers [3]. The enthusiasm of the community in welcoming this platform is so great, lot of comment about smartphone brand stated by public is an interesting thing to be processed to be information. Utilization of that information requires analytical techniques so that the produced information can help many parties. The method used in this study is Naïve Bayes classification method which is a learning technique for data mining algorithms that uses probability and statistical methods [4]. This method is used to classify comments given by the community to smartphone brands. The comments given in this application will later be classified into positive, negative, and neutral comments. The purpose of this study was to find out how much positive, negative and neutral comments the community gave to smartphone brands, so that later it would facilitate the smartphone brand in providing policies or development in the future.


Smartphone; Classification; Comment; Naïve Bayes

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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.


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