Using yelp to find romance in the city: A case of restaurants in four cities

Sohrab Rahimi, Clio Andris, Xi Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Romantic relationships are an understudied aspect of cities and the built environment. Yet, restaurants continue to attract couples and augment the landscape with visible signs of affection at a table for two—or more. User-generated content (UGC) of restaurant reviews from online review site Yelp (http://yelp.com) provide text on romantic keywords such as “date”, “love”, “boyfriend”, “wife”, “anniversary”, “family” by geolocated restaurants. We use these to distinguish restaurants and discover features of restaurants associated with various romantic keywords. These features include restaurant ratings and location, as well as comments about the ambiance, food, service, etc. Using data from the Yelp Dataset Challenge in U.S. cities Charlotte, NC, Las Vegas, NM, Phoenix, AZ, and Pittsburgh, PA, we employ different data mining and correlation tools as well as GIS modeling to learn more about what types of romantic relationships use which parts of the city, and how their choices of restaurants differ by relationship stage. We find that families prefer restaurants that are outside of the central business district (CBD), have good service and high-rated food, while couples—married or dating—prefer hot spots with great ambiance for nightlife. We also find that inexpensive food is not associated with romantic dates, and the quality of service also plays a secondary role to a “classy” and “cozy” atmosphere.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450354950
DOIs
StatePublished - Nov 7 2017
Event3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017 - Redondo Beach, United States
Duration: Nov 7 2017Nov 10 2017

Publication series

NameProceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017
Volume2017-January

Other

Other3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017
CountryUnited States
CityRedondo Beach
Period11/7/1711/10/17

Fingerprint

Personnel rating
Geographic information systems
Data mining
Quality of service
Industry

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Civil and Structural Engineering

Cite this

Rahimi, S., Andris, C., & Liu, X. (2017). Using yelp to find romance in the city: A case of restaurants in four cities. In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017 (Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017; Vol. 2017-January). Association for Computing Machinery, Inc. https://doi.org/10.1145/3152178.3152181
Rahimi, Sohrab ; Andris, Clio ; Liu, Xi. / Using yelp to find romance in the city : A case of restaurants in four cities. Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017. Association for Computing Machinery, Inc, 2017. (Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017).
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abstract = "Romantic relationships are an understudied aspect of cities and the built environment. Yet, restaurants continue to attract couples and augment the landscape with visible signs of affection at a table for two—or more. User-generated content (UGC) of restaurant reviews from online review site Yelp (http://yelp.com) provide text on romantic keywords such as “date”, “love”, “boyfriend”, “wife”, “anniversary”, “family” by geolocated restaurants. We use these to distinguish restaurants and discover features of restaurants associated with various romantic keywords. These features include restaurant ratings and location, as well as comments about the ambiance, food, service, etc. Using data from the Yelp Dataset Challenge in U.S. cities Charlotte, NC, Las Vegas, NM, Phoenix, AZ, and Pittsburgh, PA, we employ different data mining and correlation tools as well as GIS modeling to learn more about what types of romantic relationships use which parts of the city, and how their choices of restaurants differ by relationship stage. We find that families prefer restaurants that are outside of the central business district (CBD), have good service and high-rated food, while couples—married or dating—prefer hot spots with great ambiance for nightlife. We also find that inexpensive food is not associated with romantic dates, and the quality of service also plays a secondary role to a “classy” and “cozy” atmosphere.",
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Rahimi, S, Andris, C & Liu, X 2017, Using yelp to find romance in the city: A case of restaurants in four cities. in Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017. Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017, vol. 2017-January, Association for Computing Machinery, Inc, 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017, Redondo Beach, United States, 11/7/17. https://doi.org/10.1145/3152178.3152181

Using yelp to find romance in the city : A case of restaurants in four cities. / Rahimi, Sohrab; Andris, Clio; Liu, Xi.

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017. Association for Computing Machinery, Inc, 2017. (Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Rahimi S, Andris C, Liu X. Using yelp to find romance in the city: A case of restaurants in four cities. In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017. Association for Computing Machinery, Inc. 2017. (Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017). https://doi.org/10.1145/3152178.3152181