An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data

Ashraf Kamal, Muhammad Abulaish


Abstract
Self-deprecating sarcasm is a special category of sarcasm, which is nowadays popular and useful for many real-life applications, such as brand endorsement, product campaign, digital marketing, and advertisement. The self-deprecating style of campaign and marketing strategy is mainly adopted to excel brand endorsement and product sales value. In this paper, we propose an LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. To the best of our knowledge, there is no prior work related to self-deprecating sarcasm detection using deep learning techniques. Starting with a filtering step to identify self-referential tweets, the proposed approach adopts a deep learning model using LSTM for detecting self-deprecating sarcasm. The proposed approach is evaluated over three Twitter datasets and performs significantly better in terms of precision, recall, and f-score.
Anthology ID:
2019.icon-1.24
Volume:
Proceedings of the 16th International Conference on Natural Language Processing
Month:
December
Year:
2019
Address:
International Institute of Information Technology, Hyderabad, India
Venue:
ICON
SIG:
Publisher:
NLP Association of India
Note:
Pages:
201–210
Language:
URL:
https://aclanthology.org/2019.icon-1.24
DOI:
Bibkey:
Cite (ACL):
Ashraf Kamal and Muhammad Abulaish. 2019. An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data. In Proceedings of the 16th International Conference on Natural Language Processing, pages 201–210, International Institute of Information Technology, Hyderabad, India. NLP Association of India.
Cite (Informal):
An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data (Kamal & Abulaish, ICON 2019)
Copy Citation:
PDF:
https://aclanthology.org/2019.icon-1.24.pdf