Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm
DOI:
https://doi.org/10.47738/ijiis.v2i2.13Keywords:
Naive bayes classifier, K-means, Sentiment analysisAbstract
In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive commentsReferences
Indriati, A. Ridok. Sentiment Analysis For Review Mobile Applications Using Neighbor Method Weighted K-Nearest Neighbor (NWKNN). Journal of Environmental Engineering & Sustainable Technology. Malang. 2016.
B, Liu. 2012. Sentiment Analysis and Opinion Mining. Synthesis Lectures On Human Language Technologies, 5 (1), 1-167.
B, Davies, "Database Systems 3rd Edition", Basingstoke, UK: Palgrave, 2004.
A. Mustafa, A. Akbar, A. Sultan. Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization. International Journal of Multemedia and Ubiquitos Engineering, 4 (2), 183-188. 2009.
L. S. Ting, A. H. Tsang. Is Naive Bayes a Good Classifier for Documennt Classification?. International Journal of Software Engineering and Its Aplicatons, 5 (3), 37-46. 2011.
M. Joshi, H. Vala. Opinion Mining For Sentiment Data Classification. International Journal of Research in Information Technology, 3 (1), 1-13. 2014.
L. Agusta. Comparison algorithm Stemming Porter with Algortitular Nazief & Adriani for Stemming Bahasa Indonesia text document. National Conference of Systems and Informatics. Kns & 109-036. 2009.
Balazs, J. A., Velasquez, J. D. (2016), Opinion mining and information fusion: a survey, Information Fusion, 27, 95-110.
Boynukal?n, Z., and Karagoz, P., (2013), Emotion analysis on Turkish texts, ISCIS 2013, Lecture Notes in Electrical Engineering, Volume 264, pp. 159-168.
Catal, C., Sevim, U. and Diri, B., (2011), Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm, Expert Syst. Appl., 38, 3 (March 2011), 2347-2353.
Celik, H., (2013), Sentiment analysis for Turkish language, MSc Thesis, Istanbul Kultur University.
Cetin, M., and Amasyali, F., (2013), Active learning for Turkish sentiment analysis, INISTA 2013, Albana, Bulgaria, pp. 1-4.
Chalothorn, T., Ellman, J., (2015), Simple approaches of sentiment analysis via ensemble learning, Information Science and Applications, Lecture Notes in Electrical Engineering, Volume 339, 631-639.
Demirtas, E., and Pechenizkiy, M., (2013), Cross-lingual polarity detection with machine translation, Intl Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, NY, 8 pages.
Downloads
Published
Issue
Section
License
Authors who publish with IJIIS : International Journal on Informatics and Information Systems agree to the following terms: Authors retain copyright and grant the IJIIS : International Journal on Informatics and Information Systems right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in IJIIS : International Journal on Informatics and Information Systems. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in IJIIS : International Journal on Informatics and Information Systems. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
