Semantic Place Prediction With User Attribute in Social Media

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Abstract
In our daily life, we prefer to describe the location of a person with semantic words such as “travel,” “cinema” rather than geographic terminology such as GPS coordinates ($32^{\circ }24^{^{\prime }}12^{^{\prime \prime }}$32∘24'12''N, $2^{\circ }90^{^{\prime }}26.5^{^{\prime \prime }}$2∘90'26.5''E). This is because semantic places are easier to understand compared with precise geographic location. Existing methods only take text-image pairs into consideration, neglecting the user attribute, which is essential in semantic place prediction. In this work, we explore the impact of user attribute and propose user attribute included multimodal fusion models for semantic place prediction. Specifically, due to the complementarity of different modalities, multimodal fusion networks can adaptively learn the relationship between different modalities to fully use representational information from each modality. Besides, we propose a multi-image processing module to solve the scenario that a piece of Microblog text is attached to multiple images. Extensive experiments on our collected dataset verified the effectiveness of the proposed approach.
Funding Information
  • National Natural Science Foundation of China (61772108, 61932020, 61976083, U1908210)

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