Abstract
We propose a novel method to mine popular menu items from online reviews. In order to extract popular menu items, a crawler that uses the wrapper on search web sites was used to collect online reviews, restaurant names, and menu items. Then, unnecessary posts were removed by using the patterns. Also, post frequency was used to find the most frequently appearing menu items from online reviews in order to select the most popular menu items. In the result, the total average accuracy was 0.900. © 2011 Springer-Verlag.
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Gu, Y. H., & Yoo, S. J. (2011). Mining popular menu items of a restaurant from web reviews. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6988 LNCS, pp. 242–250). https://doi.org/10.1007/978-3-642-23982-3_31
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