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General Web search engines characterized by "onesize fits all" provide the same results for the same keyword queries even though these latter are submitted by different users with different intentions. In mobile Web search, the expected results for some queries could vary depending upon the user'slocation. We believe that identifying user's geographic intent in Web search can help to personalize search results by ranking local search results higher in the search results lists. Therefore, the objective of this paper is twofold: first to identify whether a mobile user query is location sensitive and second to personalize Web search results for these queries. In order to achieve these objectives, we propose to build a location language model for queries as a location query profile. Based on this latter, we compute two features issued from the domains of probability theory and Information theory, namely the Kurtosis and Kullback-Leibler Divergence measures in order to automatically classify location sensitive queries. The classification scheme is then integrated into a personalization process according to two approaches: refinement and re-ranking. Experimental evaluation using a sample of queries from AOL log and top documents returned by Google search, shows that the proposed model achieves high accuracy in identifying local sensitive queries and shows significant improvement on search relevance when integrated to a search engine.