Deep learning-based personality recognition from text posts of online social networks

Deep learning-based personality recognition from text posts of online social networks Personality is an important psychological construct accounting for individual differences in people. Computational personality recognition from online social networks is gaining increased research attention in recent years. However, the majority of existing methodologies mainly focused on human-designed shallow statistical features and didn’t make full use of the rich semantic information in user-generated texts, while those texts are exactly the most direct way for people to translate their internal thoughts and emotions into a form that others can understand. This paper proposes a deep learning-based approach for personality recognition from text posts of online social network users. We first utilize a hierarchical deep neural network composed of our newly designed AttRCNN structure and a variant of the Inception structure to learn the deep semantic features of each user’s text posts. Then we concatenate the deep semantic features with the statistical linguistic features obtained directly from the text posts, and feed them into traditional regression algorithms to predict the real-valued Big Five personality scores. Experimental results show that the deep semantic feature vectors learned from our proposed neural network are more effective than the other four kinds of non-trivial baseline features; the approach that utilizes the concatenation of our deep semantic features and the statistical linguistic features as the input of the gradient boosting regression algorithm achieves the lowest average prediction error among all the approaches tested by us. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Deep learning-based personality recognition from text posts of online social networks

Download PDF
15 pages
Read Article

Loading next page...
 
/lp/springer_journal/deep-learning-based-personality-recognition-from-text-posts-of-online-6YTuQ3DAHG
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-018-1212-4
Publisher site
See Article on Publisher Site

Abstract

Personality is an important psychological construct accounting for individual differences in people. Computational personality recognition from online social networks is gaining increased research attention in recent years. However, the majority of existing methodologies mainly focused on human-designed shallow statistical features and didn’t make full use of the rich semantic information in user-generated texts, while those texts are exactly the most direct way for people to translate their internal thoughts and emotions into a form that others can understand. This paper proposes a deep learning-based approach for personality recognition from text posts of online social network users. We first utilize a hierarchical deep neural network composed of our newly designed AttRCNN structure and a variant of the Inception structure to learn the deep semantic features of each user’s text posts. Then we concatenate the deep semantic features with the statistical linguistic features obtained directly from the text posts, and feed them into traditional regression algorithms to predict the real-valued Big Five personality scores. Experimental results show that the deep semantic feature vectors learned from our proposed neural network are more effective than the other four kinds of non-trivial baseline features; the approach that utilizes the concatenation of our deep semantic features and the statistical linguistic features as the input of the gradient boosting regression algorithm achieves the lowest average prediction error among all the approaches tested by us.

Journal

Applied Intelligence – Springer Journals

Published: Jun 5, 2018

References

  • A survey of personality computing
    Vinciarelli, A; Mohammadi, G
  • Personality
    Funder, DC
  • Personality: a psychological interpretation
    Allport, GW
  • Personality recognition on social media with label distribution learning
    Xue, D; Hong, Z; Guo, S; Gao, L; Wu, L; Zheng, J; Zhao, N
  • Computational personality recognition in social media
    Farnadi, G; Sitaraman, G; Sushmita, S; Celli, F; Kosinski, M; Stillwell, D; Davalos, S; Moens, MF; De Cock, M
  • Personality, gender, and age in the language of social media: the open-vocabulary approach
    Schwartz, HA; Eichstaedt, JC; Kern, ML; Dziurzynski, L; Ramones, SM; Agrawal, M; Shah, A; Kosinski, M; Stillwell, D; Seligman, ME
  • Online social networks and insights into marketing communications
    Polonsky, M
  • Emerging late adolescent friendship networks and Big Five personality traits: a social network approach
    Selfhout, M; Burk, W; Branje, S; Denissen, J; Aken, MV; Meeus, W
  • Self-report versus web-log: which one is better to predict personality of website users?
    Li, A; Yan, Z; Zhu, TS
  • Private traits and attributes are predictable from digital records of human behavior
    Kosinski, M; Stillwell, D; Graepel, T
  • Manifestations of personality in online social networks: self-reported Facebook-related behaviors and observable profile information
    Gosling, SD; Augustine, AA; Vazire, S; Holtzman, N; Gaddis, S
  • Reducing the dimensionality of data with neural networks
    Hinton, GE; Salakhutdinov, RR
  • Aspect extraction for opinion mining with a deep convolutional neural network
    Poria, S; Cambria, E; Gelbukh, A
  • Greedy function approximation: a gradient boosting machine
    Friedman, JH
  • Lecture Notes in Computer Science
    Nie, Dong; Li, Lin; Zhu, Tingshao
  • Predicting active users’ personality based on micro-blogging behaviors
    Li, L; Li, A; Hao, BB; Guan, ZD; Zhu, TS; Liu, C
  • Using linguistic cues for the automatic recognition of personality in conversation and text
    Mairesse, F; Walker, MA; Mehl, MR; Moore, RK
  • Linguistic inquiry and word count: LIWC [Computer software]
    Pennebaker, JW; Booth, RJ; Francis, ME
  • The MRC psycholinguistic database
    Coltheart, M
  • Deep learning-based document modeling for personality detection from text
    Majumder, N; Poria, S; Gelbukh, A; Cambria, E
  • Gradient-based learning applied to document recognition
    LeCun, Y; Bottou, L; Bengio, Y; Haffner, P
  • Finding structure in time
    Elman, JL
  • Long short-term memory
    Hochreiter, S; Schmidhuber, J
  • Linguistic styles: language use as an individual difference
    Pennebaker, JW; King, LA
  • The psychological meaning of words: LIWC and computerized text analysis methods
    Tausczik, YR; Pennebaker, JW
  • Distributional structure
    Harris, ZS
  • Dropout: a simple way to prevent neural networks from overfitting
    Srivastava, N; Hinton, GE; Krizhevsky, A; Sutskever, I; Salakhutdinov, R
  • A tutorial on support vector regression
    Smola, AJ; Schölkopf, B
  • Random forests
    Breiman, L

You’re reading a free preview. Subscribe to read the entire article.

Try 2 weeks free now

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

or browse the journals available

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off

Sign up
for free
Start 14 day
Free Trial