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
- Editors:
- Dipti Misra Sharma, Pushpak Bhattacharya
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India
- Note:
- Pages:
- 201–210
- Language:
- URL:
- https://aclanthology.org/2019.icon-1.24
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2019.icon-1.24.pdf