The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Deep Learning-Based Document Modeling for Personality Detection from Text
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Personality Structure: Emergence of the Five-Factor Model
Digman, J. M. (1990) · Annual Review of Psychology · doi:10.1146/annurev.ps.41.020190.002221 · Cited by 6,436
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
The structure of phenotypic personality traits.
Goldberg, L. R. (1993) · American Psychologist · doi:10.1037/0003-066x.48.1.26 · Cited by 4,816
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
Mohammad, S. M., & Turney, P. D. (2013) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00460.x · Cited by 2,634
The MRC Psycholinguistic Database
Coltheart, M. (1981) · The Quarterly Journal of Experimental Psychology Section A · doi:10.1080/14640748108400805 · Cited by 2,437
Linguistic styles: language use as an individual difference
Pennebaker, J. W., & King, L. A. (1999) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.77.6.1296 · Cited by 1,961
Affective Computing and Sentiment Analysis
Cambria, E. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.31 · Cited by 1,204
Self-citationUsing linguistic cues for the automatic recognition of personality in conversation and text
Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2349 · Cited by 1,046
Cited by
A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226
Context-Dependent Sentiment Analysis in User-Generated Videos
Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1081 · Cited by 939
Self-citationA Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Do, H. H., Prasad, P., Maag, A., & Alsadoon, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.003 · Cited by 614
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Ma, Y., Peng, H., & Cambria, E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12048 · Cited by 592
Self-citation
Metadata
- Authors
- Majumder, N., Poria, S., Gelbukh, A., & Cambria, E.
- Venue
- IEEE Intelligent Systems
- Published
- 2017-03
- Volume
- 32
- Issue
- 2
- Pages
- 74-79
- ISSN
- 1541-1672 (ISSN-L)
- OpenAlex
- W2599743206
- Cited by
- 675
- References
- 19
- FWCI
- 47.07
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning-Based Document Modeling for Personality Detection from Text. https://app.cochat.ai/papers/10.1109/mis.2017.23
- The paper
- Majumder, N., Poria, S., Gelbukh, A., & Cambria, E. (2017). Deep Learning-Based Document Modeling for Personality Detection from Text. IEEE Intelligent Systems. https://doi.org/10.1109/mis.2017.23
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
