Abstract
A meme is a pictorial representation of an idea or theme. In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression. However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task. In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU_KDE_MA employs an attention-based neural network model to tackle the problem. Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words. Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences. The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively. Experimental results show that our proposed neural model obtained competitive performance among the participants’ systems.- Anthology ID:
- 2020.semeval-1.146
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1106–1111
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.146
- DOI:
- 10.18653/v1/2020.semeval-1.146
- Cite (ACL):
- Abu Nowshed Chy, Umme Aymun Siddiqua, and Masaki Aono. 2020. CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1106–1111, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis (Chy et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.semeval-1.146.pdf