Abstract
E-commerce reviews are very helpful for customers to know other people's opinions on interested products. Meanwhile, producers are able to learn the public sentiment on their products being sold in E-commerce platforms. Generally, E-commerce reviews involve many aspects of products, e.g., appearance, quality, price, logistics, and so on. In this paper, we define each of those aspects as a dimension of product, and present a dimension-based sentiment analysis approach for E-commerce reviews. In particular, we employ a dimensional sentiment lexicon expansion mechanism to remove the sentiment word ambiguity among different dimensions, and propose a rules and dimensional sentiment lexicon based algorithm for sentiment analysis on E-commerce reviews. We conduct experiments on a large-scale product reviews dataset involving over 28 million reviews, and compare our dimension-based sentiment analysis approach with the traditional way that does not consider dimensions of reviews. The results show that the multi-dimensional approach outperforms the traditional way in terms of F-measure.
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