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Mining Popular Menu Items of a Restaurant from Web Reviews

机译:从网络评论中挖掘餐厅的热门菜单项

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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.
机译:我们提出了一种新颖的方法来从在线评论中挖掘受欢迎的菜单项。为了提取受欢迎的菜单项,使用了在搜索网站上使用包装程序的爬虫来收集在线评论,餐厅名称和菜单项。然后,使用这些模式删除了不必要的帖子。同样,发布频率被用来从在线评论中查找最频繁出现的菜单项,以便选择最受欢迎的菜单项。结果,总平均准确度为0.900。

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