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Title
Abstract
Key Takeaways
Introduction
Manual/Questionnaire Approach
Mbti Signals in Twitter Data
Conclusions
References
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Computer Science
Computational Linguistics

A Survey of Automatic Personality Detection from Texts

Profile image of Seren YenikentSeren Yenikent

2020

Abstract

Personality profiling has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this survey, we show the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point out what has been gained and what lost during that trajectory, and show what can be realistic expectations in the field.

Key takeaways
sparkles

AI

  1. Automatic personality detection has evolved from psychology-based methods to NLP techniques using social media data.
  2. The Big 5 model offers continuous trait assessment, while MBTI uses discrete typologies, impacting detection accuracy.
  3. Research indicates that personality detection models trained on Facebook data outperform those trained on Twitter data.
  4. Ethical concerns arise from using social media data for personality profiling without user consent, risking algorithmic bias.
  5. Future research should bridge psychology and NLP to set realistic expectations for personality detection from textual data.

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