Semantic-enhanced sequential modeling for personality trait recognition from texts

The automatic recognition of personality traits from texts has attracted significant attention. Existing studies typically combine linguistic feature engineering with traditional models, use five various neural networks to predict personality traits with multiple labels, and fail to achieve the best performance on each label. To this end, in this paper, we propose a novel semantic-enhanced personality recognition neural network (SEPRNN) model, which has a goal of avoiding dependence on feature engineering, allowing the same model to adapt to detecting five various personality traits with no modification to the model itself, and employing deep learning based methods and atomic features of texts to build vectorial word-level representation for personality trait recognition. Specifically, to precisely recognize multi-labeled personality traits, we first propose a word-level semantic representation for texts based on context learning. Then, a fully connected layer is used to obtain higher-level semantics of texts. Finally, the experimental results demonstrate that the proposed approach achieves significant performance improvement for multi-labeled personality traits compared with several baselines.