Identifying the activities supported by locations with community-authored content

ABSTRACT

Community-authored content, such as location specific reviews, offers a wealth of information about virtually every imaginable location today. In this work, we process Yelp's community-authored reviews to identify a set of potential activities that are supported by the location reviewed. Using 14 test locations we show that the majority of the 40 most common results per location (determined by verb-noun pair frequency) are actual activities supported by their respective locations, achieving a mean precision of up to 79.3%. Although the number of reviews authored for a location has a strong influence on precision, we are able to achieve a precision up to 29.5% when processing only the first 50 reviews, increasing to 45.7% and 57.3% for the first 100 and 200 reviews, respectively. In addition, we present two context-aware services that leverage location-based activity information on a city scale that is accessible through a Web service we developed supporting multiple cities in North America.

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Index Terms

• Information systems

  • Information retrieval

  • Information storage systems