Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and bert model
Sarcasm is a complicated linguistic term commonly found in e-commerce and social media sites. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several...
Saved in:
Main Authors: | Eke, Christopher Ifeanyi, Norman, Azah Anir, Shuib, Liyana |
---|---|
Format: | Article |
Published: |
Institute of Electrical and Electronics Engineers
2021
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/26991/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
by: Eke, Christopher Ifeanyi, et al.
Published: (2021) -
Multi-feature fusion framework for automatic sarcasm identification in Twitter data / Christopher Ifeanyi Eke
by: Christopher , Ifeanyi Eke
Published: (2021) -
Sarcasm detection in Persian
by: Nezhad, Zahra Bokaee, et al.
Published: (2021) -
Hyperpartisan News and Articles Detection Using BERT and ELMo
by: Huang, Gerald Ki Wei, et al.
Published: (2020) -
Building Standard Offline Anti-phishing Dataset for
Benchmarking
by: Chiew, Kang Leng, et al.
Published: (2018)