@inproceedings{d60524868afd48ea9f7bac542c63d4cc,
title = "Mining e-commerce feedback comments for dimension rating profiles",
abstract = "Opinion mining on regular documents like movie reviews and product reviews has been intensively studied. In this paper we focus on opinion mining on short e-commerce feedback comments.We aim to compute a comprehensive rating profile for sellers comprising of dimension ratings and weights. We propose an algorithm to mine feedback comments for dimension ratings, combining opinion mining and dependency relation analysis, a recent development in natural language processing. We formulate the problem of computing dimension weights from ratings as a factor analytic problem and propose an effective solution based on matrix factorisation. Extensive experiments on eBay and Amazon data demonstrate that our proposed algorithms can achieve accuracies of 93.1% and 89.64% respectively for identifying dimensions and ratings in feedback comments, and the weights computed can accurately reflect the amount of feedback for dimensions.",
author = "Lishan Cui and Xiuzhen Zhang and Yan Wang and Lifang Wu",
year = "2013",
doi = "10.1007/978-3-642-53914-5_1",
language = "English",
isbn = "9783642539138",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "1--12",
editor = "Hiroshi Motoda and Zhaohui Wu and Longbing Cao and Osmar Zaiane and Min Yao and Wei Wang",
booktitle = "Advanced Data Mining and Applications",
address = "United States",
note = "9th International Conference on Advanced Data Mining and Applications, ADMA 2013 ; Conference date: 14-12-2013 Through 16-12-2013",
}