Emotion and personality analysis and detection using natural language processing, advances, challenges and future scope

Emotion detection from text is a relatively new sub-field of artificial intelligence closely related to Sentiment Analysis (SA). SA detects positive, neutral, or negative emotions in text. In contrast, emotion analysis detects and distinguishes certain types of emotions expressed in textbooks, such...

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Bibliographic Details
Published in:The Artificial intelligence review 2023-12, Vol.56 (Suppl 3), p.3273-3297
Main Authors: Safari, Faezeh, Chalechale, Abdolah
Format: Article
Language:English
Subjects:
Artificial Intelligence
Chatbots
Computational linguistics
Computer Science
Data mining
Datasets
Deep learning
Detectors
Emotion recognition
Emotions
Happiness
Keywords
Language
Language processing
Linguistics
Machine learning
Natural language interfaces
Natural language processing
Personality
Personality traits
Semantics
Sentiment analysis
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Summary:Emotion detection from text is a relatively new sub-field of artificial intelligence closely related to Sentiment Analysis (SA). SA detects positive, neutral, or negative emotions in text. In contrast, emotion analysis detects and distinguishes certain types of emotions expressed in textbooks, such as disgust, fear, anger, happiness, surprise and sadness. Meanwhile, personality is a critical psychological concept that accounts for unique characteristics. Identifying and validating an individual’s personality efficiently and reliably is an admirable goal. This article aims to present a simultaneous review of Emotion and Personality detection from texts and elaborates upon approaches in developing text-based Emotion and Personality detection systems. The studies’ essential contributions, methodologies, datasets, conclusions drawn, strengths, and limitations are also explored. Additionally, this article discusses some of the field’s state-of-the-art ideas. In conclusion, the study delves into specific challenges and possible future research directions for detecting emotions and personalities from the text.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10603-3