Abstract
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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-021-02277-7/MediaObjects/10489_2021_2277_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-021-02277-7/MediaObjects/10489_2021_2277_Fig2_HTML.png)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Li Y, Zhu T, Li A, Fan Z (2011) Web behavior and personality: A review. In: Web Society
Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, Agrawal M, Shah A, Kosinski M, Stillwell D, Seligman MEP (2013) Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. Plos One 8(9):e73791
Qiu L, Lu J, Ramsay JE, Yang S, Qu W, Zhu T (2017) Personality expression in Chinese language use. Int J Psychol 52(6):463–472
Mccrae RR, Costa PT (1987) Validation of the Five-Factor Model of Personality Across Instruments and Observers. J Personal Soc Psychol 52(1):81–90
Roth PL, Bobko P, Van Iddekinge CH, Thatcher JB (2016) Social Media in Employee-Selection-Related Decisions A Research Agenda for Uncharted Territory. J Manag 42(1):269–298
Tett RP, Jackson DN, Rothstein M (1991) Personality Measures as Predictors of Job Performance: A Meta-Analytic Review. Person Psychol 44(4):703–742
Barrick MR, Mount MK (1991) The big five personality dimensions and job performance: a meta-analysis. Person Psychol 44(1):1–26
Judge TA, Higgins CA, Thoresen CJ, Barrick MR (1999) The big five personality traits, general mental ability, and career success across the life span. Person Psychol 52(3):621–652
Digman JM (1990) Personality Structure: Emergence of the Five-Factor Model. Ann Rev Psychol 41(1):417–440
Mairesse F, Walker MA, Mehl MR, Moore RK (2007) Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Intell Res 30(1):457–500
Nguyen T, Phung DQ, Adams B, Venkatesh S (2011) Towards Discovery of Influence and Personality Traits through Social Link Prediction. In: Proceedings of the Fifth International Conference on Weblogs and Social Media. Barcelona, Catalonia, Spain
Tadesse MM, Lin H, Xu B, Yang L (2018) Personality Predictions Based on User Behavior on the Facebook Social Media Platform. IEEE Access 6:61959–61969
Majumder N, Poria S, Gelbukh AF, Cambria E (2017) Deep Learning-Based Document Modeling for Personality Detection from Text. IEEE Intell Syst 32(2):74–79
Lai S, Xu L, Liu K, Zhao J (2015) Recurrent Convolutional Neural Networks for Text Classification. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2267–2273. http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745
Sun X, Liu B, Cao J, Luo J, Shen X (2018) Who Am I? Personality Detection Based on Deep Learning for Texts. In: 2018 IEEE International Conference on Communications, ICC 2018, Kansas City, pp 1–6
Xue X, Feng J, Gao Y, Liu M, Zhang W, Sun X, Zhao A, Guo SX (2019) Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction. Entropy 21 (12):1227
Xue X, Gao y, Liu M, Sun X, Zhang W, Fend J (2021) GRU-based capsule network with an improved loss for personnel performance prediction. Applied Intelligence. pages 1–14
Teng M, Zhu H, Liu C, Zhu C, Xiong H (2019) Exploiting the Contagious Effect for Employee Turnover Prediction. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, pp 1166–1173
James W P, Laura A K (1999) Linguistic styles: language use as an individual difference. Journal of personality and social psychology 77(6):1296–1312
Ross C, Orr ES, Sisic M, Arseneault JM, Simmering MG, Orr RR (2009) Personality and motivations associated with Facebook use. Comput Hum Behav 25(2):578–586
Mohammad Saif M., Kiritchenko S (2013) Using Nuances of Emotion to Identify Personality. CoRR, arXiv:1309.6352
Wei H, Zhang F, Yuan NJ, Cao C, Fu H, Xie X, Rui Y, Ma W-Y (2017) Beyond the Words: Predicting User Personality from Heterogeneous Information. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, pp 305–314
Yu J, Markov K (2017) Deep learning based personality recognition from Facebook status updates. In: IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017, Taichung, pp 383–387
Wang S, Huang M, Deng Z (2018) Densely Connected CNN with Multi-scale Feature Attention for Text Classification. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, Stockholm, pp 4468–4474
Ren Y, Zhang Y, Zhang M, Ji D (2016) Context-Sensitive Twitter Sentiment Classification Using Neural Network. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, pp 215–221
Wang SI, Manning CD (2012) Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. In: The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Korea - Volume 2: Short Papers, pp 90–94
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, Lake Tahoe, pp 1106–1114
Tang D, Qin B, Liu T (2015) Document Modeling with Gated Recurrent Neural Network for Sentiment Classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, pp 1422–1432
Lecun Y, Bottou L (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Elman JL (1990) Finding Structure in Time. Cogn Sci 14(2):179–211
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A Convolutional Neural Network for Modelling Sentences In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, Baltimore, Volume 1: Long Papers, pp 655–665
Zhao Z, Yang Z, Luo L, Lin H, Wang J (2016) Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinform 32(22):3444–3453
Zhang Q, Wang Y, Gong Y, Huang X (2016) Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, pp 836–845
Su Y, Liu Q, Liu Q, Huang Z, Yin Y, Chen E, Ding CHQ, Wei S, Hu G (2018) Exercise-Enhanced Sequential Modeling for Student Performance Prediction. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, pp 2435–2443
Cho K, van Merrienboer B, Gülçehre Ç, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, A meeting of SIGDAT, a Special Interest Group of the ACL, pp 1724–1734
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed Representations of Words and Phrases and their Compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, Lake Tahoe, pp 3111–3119
Pennington J, Socher R, Manning CD (2014) Glove: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, A meeting of SIGDAT, a Special Interest Group of the ACL, pp 1532–1543
Biel J-I, Tsiminaki V, Dines J, Gatica-Perez D (2013) Hi YouTube!: personality impressions and verbal content in social video. In: Proceedings of the 15th ACM on International conference on multimodal interaction, ACM, pp 119–126
Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9(8):1735–1780
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is All you Need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, pp 5998–6008
Mohammad SM, Kiritchenko S (2015) Using Hashtags to Capture Fine Emotion Categories from Tweets. Comput Intell 31(2):301–326
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5-6):602–610
Markovikj D, Gievska S, Kosinski M, Stillwell D (2013) Mining Facebook Data for Predictive Personality Modeling. In: ICWSM 2013
Balmaceda Jose M, Schiaffino SN, Godoy D (2014) How do personality traits affect communication among users in online social networks?. Online Inf Rev 38(1):136–153
Yin H, Wang Y, Li Q, Xu W, Yu Y, Zhang T (2018) A Network-enhanced Prediction Method for Automobile Purchase Classification using Deep Learning. In: 22nd Pacific Asia Conference on Information Systems, PACIS 2018, Yokohama, pp 111
Yang H-C, Huang Z-R (2019) Mining personality traits from social messages for game recommender systems. Knowl-Based Syst 165:157–168
Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh AF (2019) Sentiment and Sarcasm Classification With Multitask Learning. IEEE Intell Syst 34(3):38–43
Shlomo A, Dhawle S, Moshe K, Pennebaker JW (2005) Lexical Predictors of Personality Type. In: 2005 Joint Annual Meeting of the Interface and the Classification Society of North America
Oberlander J, Nowson S (2006) Whose Thumb Is It Anyway? Classifying Author Personality from Weblog Text. In: ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney
Tausczik YR, Pennebaker JW (2009) The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. J Lang Soc Psychol 29(1):24–54
Yarkoni T (2010) Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers. J Res Person 44(3):363–373
Poria S, Gelbukh AF, Agarwal B, Cambria E, Howard N (2013) Common Sense Knowledge Based Personality Recognition from Text. In: Advances in Soft Computing and Its Applications - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico City, Proceedings, Part II, pp 484–496
Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci USA 110(15):5802–5805
Youyou W, Kosinski M, Stillwell D (2015) Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci USA 112(4):1036–1040
O”Connor BP (2002) A quantitative review of the comprehensiveness of the five-factor model in relation to popular personality inventories. Assessment 9(2):188–203
Goldberg LR, Johnson JA, Eber HW, Hogan R, Ashton MC, Cloninger CR, Gough HG (2006) The international personality item pool and the future of public-domain personality measures. J Res Personal 40(1):84–96
Mehta Y, Majumder N, Gelbukh AF, Cambria E (2020) Recent trends in deep learning based personality detection. Artif Intell Rev 53(4):2313–2339
Vinciarelli A, Mohammadi G (2014) A Survey of Personality Computing. IEEE Trans Affect Comput 5(3):273–291
Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325–338
Basiri Mohammad E, Nemati S, Abdar M, Cambria E, Acharya UR (2021) ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Fut Gener Comput Syst 115:279–294
Sun Y, Zhuang F, Zhu H, Song X, He Q, Xiong H (2019) The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, pp 1625–1633
Acknowledgements
This work was sponsored by the Key Research and Development Program in Shaanxi Province of China (No.2019ZD LGY03-10) and the National Natural Science Foundation Projects of China (No.61877050).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xue, X., Feng, J. & Sun, X. Semantic-enhanced sequential modeling for personality trait recognition from texts. Appl Intell 51, 7705–7717 (2021). https://doi.org/10.1007/s10489-021-02277-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-02277-7