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摘要
Given the social network short text native sparsity, semantic inference becomes an infeasible task for conventional topic models. By exploiting the spatial and temporal characteristics of social network data, we propose a social network short text semantic modeling method, named by Spatial and Temporal Topic Model (STTM). To further overcome short text sparsity, STTM leverages co-occurrence word-word pair to reduce the sparsity problem, and moreover, it incorporates time information into the process of topics modeling in order to generate topics with higher quality. Experimental results over four real social media datasets verify the effectiveness of STTM. Published by Elsevier B.V.
