Classifying Second Life Player Gender Using Chat Data
نویسنده
چکیده
The goal of this study was to predict the genders of players of the online game Second Life using linguistic patterns from their chat data. This was accomplished using a rich set of stylistic features combined with various machine learning models. This project builds upon a previous study done at Stanford’s Virtual Human Interaction Lab in which very few linguistic features were used. Results show that adding the new features significantly improves prediction accuracy.
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