Tiếp cận phương pháp máy học trong khai thác ý kiến khách hàng  trực tuyến

Phụng Thái Kim; Tế Nguyễn An; Hà Trần Thị Thu

doi:10.24311/jabes/2019.30.10.1270

Authors

Phụng Thái Kim Trường Đại học Kinh tế TP. Hồ Chí Minh Author
Tế Nguyễn An Trường Đại học Kinh tế TP. Hồ Chí Minh Author
Hà Trần Thị Thu Trường Đại học Kinh tế Quốc dân Author

DOI:

https://doi.org/10.24311/jabes/2019.30.10.1270

Keywords:

Opinion mining, Opinion classification, Opinion classification using machine learning

Abstract

The study was conducted to apply supervised machine learning methods in mining online customer reviews. First, the study automatically collects 15,480 traveler reviews on hotels in Vietnam on Agoda.com website. Then, this study conducts the training process with machine learning models in order to find out the best model which is compatible with the training dataset and apply this model to forecast opinions for entire collected data. The results show that Logistic Regression (LR) and Support Vector Machines (SVM) methods have the best performance in Vietnamese language. This study is valuable as a reference for applications of opinion mining in the field of business.

References

Ali, A. S. A. (2015). Opinion mining techniques. International Journal of Innovative Science, Engineering and Technology  IJISET, 2(6), 752–755.

Binali, H., Potdar, V., & Wu, C. (2009). A state of the art opinion mining and its application domains. In 2009 IEEE International Conference on Industrial Technology, 1–6.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

Dhokrat, A., Khillare, S., & Mahender, C. N. (2015). Review on techniques and tools used for opinion mining. International Journal of Computer Applications Technology and Research, 4(6), 419–424.

Kumar, S., & Reddy, B. (2016). An analysis on opinion mining: Techniques and tools. Indian Journal of Research, 5(8), 489–492.

Le, H. P., Nguyen, T. M. H., Roussanaly, A., & Vinh, H. T. (2008). A hybrid approach to word segmentation of Vietnamese texts. In International Conference on Language and Automata Theory and Applications, 240–249.

Le, N. M., Do, B. N., Nguyen, V. D., & Nguyen, T. D. (2013). VNLP: An open source framework for Vietnamese natural language processing. In Proceedings of the Fourth Symposium on Information and Communication Technology, 88–93.

Lee, J., Park, D.-H., & Han, I. (2011). The different effects of online consumer reviews on consumers’ purchase intentions depending on trust in online shopping malls: An advertising perspective. Internet Research, 21(2), 187–206.

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.

Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(12), 1–135.

Shawe-Taylor, J., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing, 74(17), 3609–3618.

Stylios, G., Christodoulakis, D., Besharat, J., Vonitsanou, M., Kotrotsos, I., Koumpouri, A., & Stamou, S. (2010). Public opinion mining for governmental decisions. Electronic Journal of e-Government, 8(2), 202–214.

Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information Fusion, 36, 10–25.

Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D., & Keim, D. A. (2009). Visual sentiment analysis of RSS news feeds featuring the us presidential election in 2008. In Visual Interfaces to the Social and the Semantic Web (VISSW 2009). Retrieved from https://kops.uni-konstanz.de/bitstream/handle/123456789/5946/vissw2009.pdf

Yadav, S. K. (2015). Sentiment analysis and classification: A survey. International Journal of Advance Research in Computer Science and Management Studies, 3(3), 113–121.