In this article we present a methodology for classification of text from web authors, using sociolinguistic inspired text features. The proposed methodology uses a baseline text mining based feature set, which is combined with text features that quantify results from theoretical and sociolinguistic studies. Two combination approaches were evaluated and the evaluation results indicated a significant improvement in both combination cases. For the best performing combination approach the accuracy was 84.36%, in terms of percentage of correctly classified web posts.
CITATION STYLE
Simaki, V., Aravantinou, C., Mporas, I., & Megalooikonomou, V. (2015). Using sociolinguistic inspired features for gender classification of web authors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9302, pp. 587–594). Springer Verlag. https://doi.org/10.1007/978-3-319-24033-6_66
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