Geodesic
Product Reputation Mining: Bring Informative Review Summaries to Producers and Consumers
Computer Science and Information Systems, Tome 16 (2019) no. 2.

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Product reputation mining systems can help customers make their buying decision about a product of interest. In addition, it will be helpful to investigate the preferences of recently released products made by enterprises. Unlike the conventional manual survey, it will give us quick survey results on a low cost budget. In this article, we propose a novel product reputation mining approach based on three dimensional points of view that are word, sentence, and aspect–levels. Given a target product, the aspect–level method assigns the sentences of a review document to the desired aspects. The sentence–level method is a graph-based model for quantifying the importance of sentences. The word–level method computes both importance and sentiment orientation of words. Aggregating these scores, the proposed approach measures the reputation tendency and preferred intensity and selects top-k informative review documents about the product. To validate the proposed method, we experimented with review documents relevant with K5 in Kia motors. Our experimental results show that our method is more helpful than the existing lexicon–based approach in the empirical and statistical studies.
Keywords: product reputation mining, opinion mining, sentiment analysis, sentiment lexicon construction 
@article{CSIS_2019_16_2_a3,
     author = {Zhehua Piao and Sang-Min Park and Byung-Won On and Gyu Sang Choi and Myong-Soon Park},
     title = {Product {Reputation} {Mining:} {Bring} {Informative} {Review} {Summaries} to {Producers} and {Consumers}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {16},
     number = {2},
     year = {2019},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2019_16_2_a3/}
}
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Zhehua Piao; Sang-Min Park; Byung-Won On; Gyu Sang Choi; Myong-Soon Park. Product Reputation Mining: Bring Informative Review Summaries to Producers and Consumers. Computer Science and Information Systems, Tome 16 (2019) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2019_16_2_a3/