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Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features
2020
2020 IEEE International Conference on Data Mining (ICDM)
State-of-the-art personality prediction with text data mostly relies on bottom up, automated feature generation as part of the deep learning process. More traditional models rely on hand-crafted, theory-based text-feature categories. We propose a novel deep learning-based model which integrates traditional psycholinguistic features with language model embeddings to predict personality from the Essays dataset for Big-Five and Kaggle dataset for MBTI. With this approach we achieve stateof-the-art
doi:10.1109/icdm50108.2020.00146
fatcat:6b37ki6r7rhazbh36kciiq2vry