Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Mining the web for points of interest
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Matching points of interest from different social networking sites
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Entity discovery and annotation in tables
Proceedings of the 16th International Conference on Extending Database Technology
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Points of interest (POIs) in a city are specific locations that present some significance to people; examples include restaurants, museums, hotels, theatres and landmarks, just to name a few. Due to their role in our social and economic life, POIs have been increasingly gaining the attention of location-based applications, such as online maps and social networking sites. While it is relatively easy to find on the Web basic information about a POI, such as its geographic location, telephone number and opening hours, it is more challenging to have a deeper knowledge as to what other people say about it. What if a person wants to know all the restaurants in Paris that serve good seafood and provide a kind service? Typically, the answer to this question has to be looked for on websites that let people leave comments and opinions on POIs, a time-consuming manual task that few are willing to do. This search would be better supported by search engines if information mined from opinions were available in a structured form, such as RDF. In this position paper, we describe a general approach to enrich an existing RDF repository about POIs with data obtained from social networking sites.