Location type classification using tweet content

Haibin Liu, Bo Luo, Dongwon Lee

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

13 Scopus citations

Abstract

Location context in social media plays an important role in many applications. In addition to explicit location sharing via popular check in service, user-posted content could also implicitly reveals users location context. Identifying such a location context based on content is an interesting problem because it is not only important in inferring social ties between people, but also vital for applications such as user profiling and targeted advertising. In this paper, we study the problem of location type classification using tweet content. We extend probabilistic text classification models to incorporate temporal features and user history information in terms of probabilistic priors. Experimental results show that our extensions can boost classification accuracy effectively.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages232-237
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume1

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Education

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  • Cite this

    Liu, H., Luo, B., & Lee, D. (2012). Location type classification using tweet content. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 232-237). [6406574] (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 1). https://doi.org/10.1109/ICMLA.2012.47