skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Transformer models for text-based emotion detection: a review of BERT-based approaches

The Artificial intelligence review, 2021-12, Vol.54 (8), p.5789-5829 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-021-09958-2

Full text available

Citations Cited by
  • Title:
    Transformer models for text-based emotion detection: a review of BERT-based approaches
  • Author: Acheampong, Francisca Adoma ; Nunoo-Mensah, Henry ; Chen, Wenyu
  • Subjects: Analysis ; Architecture ; Artificial Intelligence ; Coders ; Computational linguistics ; Computer Science ; Emotion recognition ; Emotions ; Language ; Language processing ; Natural language interfaces ; Natural language processing ; Neural networks ; Sentiment analysis ; Transformers
  • Is Part Of: The Artificial intelligence review, 2021-12, Vol.54 (8), p.5789-5829
  • Description: We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT’s strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models.
  • Publisher: Dordrecht: Springer Netherlands
  • Language: English
  • Identifier: ISSN: 0269-2821
    EISSN: 1573-7462
    DOI: 10.1007/s10462-021-09958-2
  • Source: ProQuest One Psychology
    AUTh Library subscriptions: ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait