Abstract
Profile inference of SNS users is valuable for marketing, target advertisement, and opinion polls. Several studies examining profile inference have been reported to date. Although information of various types is included in SNS, most such studies only use text information. It is expected that incorporating information of other types into text classifiers can provide more accurate profile inference. As described in this paper, we propose combined method of text processing and image processing to improve gender inference accuracy. By applying the simple formula to combine two results derived from a text processor and an image processor, significantly increased accuracy was confirmed.
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CITATION STYLE
Sakaki, S., Miura, Y., Ma, X., Hattori, K., & Ohkuma, T. (2014). Twitter User Gender Inference Using Combined Analysis of Text and Image Processing. In V and L Net 2014 - 3rd Annual Meeting of the EPSRC Network on Vision and Language and 1st Technical Meeting of the European Network on Integrating Vision and Language, A Workshop of the 25th International Conference on Computational Linguistics, COLING 2014 - Proceedings (pp. 54–61). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-5408
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