Abstract
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users’ written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state-of-the-art techniques with 8 times fewer data.
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