CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1106–1111 Barcelona, Spain (Online), December 12, 2020. 1106 CSECU KDE MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis Abu Nowshed Chy1, Umme Aymun Siddiqua2, and Masaki Aono3 Department of Computer Science and Engineering 1University of Chittagong, Chattogram-4331, Bangladesh 2Asian University for Women, Chattogram-4000, Bangladesh 3Toyohashi University of Technology, Aichi, 441-8580, Japan. nowshed@cu.ac.bd, umme.siddiqua@auw.edu.bd, and aono@tut.jp 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. 1 Introduction Nowadays, social media platforms such as Facebook, Instagram, and Twitter become the most popular information sharing medium among the people due to its convenient features and realtime behavior. People usually use the various modalities of information such as textual, visual, and audio to express their views, opinions, breaking news, and ideas here. Due to the robust feature of social media, researchers and companies are trying to distill various kinds of information from its contents. But most of the previous studies address only one modality i.e. image information extraction addressed by the computer vision community and textual information extraction by the natural language processing community. However, with the growing ubiquity of Internet memes on social media, it is important to extract information from memes. We need to employ a hybrid approach in this regard since a meme comprises of an image with textual information. Memotion analysis (Sharma et al., 2020) is commonly defined as the process of detecting and analyzing the underlying emotion of a meme. It might have a significant impact on addressing various issues related to social media. For example, evil-minded people nowadays use the meme contents to propagate anti-social behavior including online harassment, cyber-bullying, and hate speech. Therefore, memotion analysis might help to limit these anti-social behaviors. To address the challenges of memotion analysis on social media contents, (Sharma et al., 2020) proposed the task 8 at SemEval-2020. The task focuses on three related subtasks. Task A defines a sentiment classification problem where a system needs to predict whether a meme content is positive, negative, or neutral. Whereas task …

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