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http://dx.doi.org/10.15207/JKCS.2017.8.4.001

User Sentiment Analysis on Amazon Fashion Product Review Using Word Embedding  

Lee, Dong-yub (Dept. of Computer Science and Engineering, Korea University)
Jo, Jae-Choon (Dept. of Computer Science and Engineering, Korea University)
Lim, Heui-Seok (Dept. of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.8, no.4, 2017 , pp. 1-8 More about this Journal
Abstract
In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.
Keywords
Word Embedding; Sentiment Analysis; Opinion Mining; Artificial Intelligence; Deep Learning; Convergence technique;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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