Mining the blogosphere to generate cuisine hotspot maps

Choosing a restaurant is one of the most frequent decisions faced in modern daily life; however, it is difficult for consumers to choose between food/restaurant by reading large amounts of reviews. This study attempts to generate cuisine hotspot maps through blog content mining to help consumers make restaurant decisions by specialties. The main obstacle in doing this involves recognizing and extracting restaurants and essentialrestaurant information (i.e., restaurant dishes) in unstructured content. In contrast to traditional Named Entity Recognition (NER) targets, dish name is a promising target that received little attention in previous studies. This study develops methods for recognizing and extracting restaurant names and dish names from review postsin the blogosphere and achieves satisfactory performance. Based on the method, we processed more than 12,000 Chinese blog posts and generated a cuisine hotspot map. The map shows the most popular dishes of restaurants in a map-view to help consumers make restaurant decisions. A prototype of cuisine hotspot map, named CuisineGuide, is implemented and available as an iPhone application.