Browse > Article
http://dx.doi.org/10.14400/JDC.2019.17.4.115

Sentiment analysis of online food product review using ensemble technique  

Kim, Han-Min (Business School, Sungkyunkwan University)
Park, Kyungbo (Business School, Sungkyunkwan University)
Publication Information
Journal of Digital Convergence / v.17, no.4, 2019 , pp. 115-122 More about this Journal
Abstract
In the online marketplace, consumers are exposed to various products and freely express opinions. As consumer product reviews have a important effect on the success of online markets and other consumers, online market needs to accurately analyze the consumers' emotions about their products. Text mining, which is one of the data analysis techniques, can analyze the consumer's reviews on the products and efficiently manage the products. Previous studies have analyzed specific domains and less than 20,000 data, despite the different accuracy of the analysis results depending on the data domain and size. Further, there are few studies on additional factors that can improve the accuracy of analysis. This study analyzed 72,530 review data of food product domain that was not mainly covered in previous studies by using ensemble technique. We also examined the influence of summary review on improving accuracy of analysis. As a result of the study, this study found that Boosting ensemble technique has the highest accuracy of analysis. In addition, the summary review contributed to improving accuracy of the analysis.
Keywords
Text mining; Sentiment analysis; Ensemble technique; Online market; Machine learning;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 G. Kim & H. Koo. (2016). The causal relationship between risk and trust in the online marketplace: A bidirectional perspective. Computers in Human Behavior, 55, 1020-1029. DOI : 10.1016/j.chb.2015.11.005   DOI
2 P. A. Pavlou & D. Gefen. (2004). Building effective online marketplaces with institution-based trust. Information systems research, 15(1), 37-59. DOI : 10.1287/isre.1040.0015   DOI
3 W. Fan, L. Wallace, S. Rich & Z. Zhang. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), 76-82. DOI : 10.1145/1151030.1151032   DOI
4 D. Paranyushkin. (2011). Identifying the pathways for meaning circulation using text network analysis. Berlin: Nodus Labs.
5 J. H. Ryu & Y. Y. You. (2018). The Fourth Industrial Revolution Core Technology Association Analysis Using Text Mining. Journal of Digital Convergence, 16(8), 129-136. DOI : 10.14400/JDC.2018.16.8.129   DOI
6 C. Rodriguez-Penagos, J. A. Batalla, J. Codina-Filba, D. Garcia-Narbona, J. Grivolla, P. Lambert & R. Sauri. (2013). FBM: Combining lexicon-based ML and heuristics for Social Media Polarities. In Second Joint Conference on Lexical and Computational Semantics (*SEM). Proceedings of the Seventh International Workshop on Semantic Evaluation. (pp. 483-489) Atlanta. Georgia.
7 Y. Su, Y. Zhang, D. Ji, Y. Wang & H. Wu. (2012). Ensemble learning for sentiment classification. In Workshop on Chinese Lexical Semantics. (pp. 84-93). July, Berlin, Heidelberg.
8 E. Y. Kim & E. J. Ko. (2018). Monitoring Mood Trends of Twitter Users using Multi-modal Anal ysis method of Texts and Images. Journal of the Korea Convergence Society, 9(1), 419-431. DOI : 10.15207/JKCS.2018.9.1.419   DOI
9 J. H. Bae, J. E. Son & M. Song. (2013). Analysis of twitter for 2012 South Korea presidential election by text mining techniques. Journal of Intelligence and Information Systems, 19, 141-156. DOI : 10.13088/jiis.2013.19.3.141
10 D. Y. Lee, J. C. Jo & H. S. Lim. (2017). User Sentiment Analysis on Amazon Fashion Product Review Using Word Embedding. Journal of the Korea Convergence Society, 8(1), 11. DOI : 10.15207/JKCS.2017.8.4.001
11 L. Rokach. (2010). Pattern classification using ensemble methods. World Scientific.
12 G. Wang, J. Sun, J. Ma, K. Xu & J. Gu. (2014). Sentiment classification: The contribution of ensemble learning. Decision support systems, 57, 77-93. DOI : 10.1016/j.dss.2013.08.002   DOI
13 N. F. F. Da Silva, E. R. Hruschkaa & E. R. Hruschka. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170-179. DOI : 10.1016/j.dss.2014.07.003   DOI
14 C. Catal & M. Nangir. (2017). A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135-141. DOI : 10.1016/j.asoc.2016.11.022   DOI
15 A. Hassan, A. Abbasi & D. Zeng. (2013). Twitter sentiment analysis: A bootstrap ensemble framework. In Social Computing. (pp. 8-14). Alexandria. USA.
16 T. Chalothom & J. Ellman. (2015). Simple approaches of sentiment analysis via ensemble learning. In information science and applications. (pp. 631-639). Berlin, Heidelberg.
17 E. Fersini, E. Messina & F. A. Pozzi. (2014). Sentiment analysis: Bayesian ensemble learning. Decision support systems, 68, 26-38. DOI : 10.1016/j.dss.2014.10.004   DOI