TY - JOUR
T1 - Deep Learning-Based Document Modeling for Personality Detection from Text
AU - Majumder, Navonil
AU - Poria, Soujanya
AU - Gelbukh, Alexander
AU - Cambria, Erik
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.
AB - This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.
KW - artificial intelligence
KW - convolutional neural network
KW - distributional semantics
KW - intelligent systems
KW - natural language processing
KW - neural-based document modeling
KW - personality
UR - http://www.scopus.com/inward/record.url?scp=85017176110&partnerID=8YFLogxK
U2 - 10.1109/MIS.2017.23
DO - 10.1109/MIS.2017.23
M3 - Artículo
SN - 1541-1672
VL - 32
SP - 74
EP - 79
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 2
M1 - 7887639
ER -