Abstract
As a branch of psychology, personality plays an important role in distinguishing individuals in the society. The existing personality prediction models need to be further improved in precision and generalization. Recently, deep neural network (DNN) models are being applied to personality prediction tasks to obtain promising results. However, only extracting the semantic features of text through deep learning is very limited to improve the performance of the model. We propose a BERT-based Model for Personality Prediction named BSAM to extract semantic features and use the statistical information of corpus as external features. In this model, we concatenate the output of BERT with the statistical information and use bidirectional long short term memory networks (Bi-LSTM), bidirectional gated recurrent unit (Bi-GRU) and improved convolutional neural networks (CNN) to extract deep semantic features. We also compare the results with benchmark models on social media datasets and test the effectiveness of statistical features. The experimental results show that our model can effectively improve the classification performance of the five dimensions of the Big-Five personality.
This work is supported by the Fundamental Research Funds for the Central Universities (N2116019) and the National Natural Science Foundation of China (U1811261).
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Xu, B., Wang, T., Gao, K., Zhang, Z. (2023). BSAM: A BERT-Based Model with Statistical Information for Personality Prediction. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_42
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DOI: https://doi.org/10.1007/978-3-031-25198-6_42
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