personality detection using bagged SVM over BERT word embedding ensembles

Accuracy of BB-SVM model with different BERT layer inputs compared to the accuracy of this model with the concatenation of the last four layers


Recently, the automatic prediction of personality traits has received increasing attention and has emerged as a hot topic within the field of affective computing. In this work, we present a novel deep learning-based approach for automated personality detection from text. We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings from textual data for automated author personality detection. Our primary goal is to develop a computationally efficient, high performance personality prediction model which can be easily used by a large number of people without access to huge computation resources. Our extensive experiments with this ideology in mind, led us to develop a novel model which feeds contextualized embeddings along with psycholinguistic features to a Bagged-SVM classifier for personality trait prediction. Our model outperforms the previous state of the art by 1.04% and, at the same time is significantly more computationally efficient to train. We report our results on the famous gold standard Essays dataset for personality detection.

Amirmohammad Kazemeini
Amirmohammad Kazemeini
Artificial Intelligence Student

I am a Natural Language Processing research student who is passionate about psychology and social science.

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