Social Profiling of Flickr: Integrating Multiple Types of Features for Gender Classification
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Authors
Eltaher, Mohammed Ali
Lee, Jeongkyu
Issue Date
2015-03-27
Type
Presentation
Language
en_US
Keywords
Social user mining , Semantic data , Classification
Alternative Title
Abstract
With the pervasive use of social media sites, an extraordinary amount of data has been generated in different data types such as text and image. Combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a powerful way of discovering unknown information about the users. However, there has been few research work reported about combination of image and text data for social user mining. In this study, we propose a novel idea to classify the gender of user by integrating multiple types of features. We utilize not only text information, i.e., tag or description, but also images posted by a user with semantic based data fusion technique.