Abstract
With advancement of technology and social media, textual data is also growing day by day. Thus, Automatic Authorship Identification (AAI), which is majorly divided into to author identification, authorship attribution and author profiling, is a growing field of research. Specifically identifying author characteristics from the content also called author profiling is gaining pace as it contributes in the areas of marketing and forensics. The PAN labs every year provides a platform for scholars to contribute by organizing the challenge of author profiling tasks. In this paper, our attempt is to predict gender and language variety for authors using English data of PAN 2017 dataset.
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