Abstract
While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in gender classification models and gender type usually varies with context. This paper proposes and validates a framework for the development of gender classification that uses a more “data-driven” approach. The framework constantly extracts content-specific features from the discussions and could automatically adjust the features selected to accommodate the contextual changes in order to achieve better classification accuracy. It does not require any manual effort for model adjustment, which makes it easier for organizations to adopt.
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Acknowledgments
This work is partly supported by the National Natural Science Foundation of PRC (Nos. 71531013, 71490720, and 71401047).
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Wang, J., Yan, X., Zhu, B. (2017). Behavior Theory Enabled Gender Classification Method. In: Fan, M., Heikkilä, J., Li, H., Shaw, M., Zhang, H. (eds) Internetworked World. WEB 2016. Lecture Notes in Business Information Processing, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-319-69644-7_20
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DOI: https://doi.org/10.1007/978-3-319-69644-7_20
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