Abstract
This paper presents a new method for visualizing online product reviews considering customer profiles. Typically, product review data are unstructured and have no fixed format or structure. The review data can be used by customers and also an e-business company. Potential consumers can acquire useful information on product characteristics and decide whether to buy or not depending on the review data. Also, the company can understand customers’ experiences or opinions on the product and reflect them in developing marketing strategies. In order to provide valuable information to the customers from enormous and unstructured review data, the process of collecting, storing, and preprocessing of review data should be performed firstly. And then text mining and personalization techniques can be integrated to extract properly visualized data. Thus, customers can utilize review data conveniently with the assistance of the proposed system.
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Acknowledgments
This work is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1D1A1B05029080).
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Kim, J., Kim, D. (2018). Personalized Information Visualization of Online Product Reviews. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_29
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DOI: https://doi.org/10.1007/978-981-10-6520-0_29
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