YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 916–921 Barcelona, Spain (Online), December 12, 2020. 916 YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis Li Yuan, Jin Wang and Xuejie Zhang School of Information Science and Engineering Yunnan University Kunming, China Contact:{wangjin, xjzhang}@ynu.edu.cn Abstract In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest. However, the clas- sification and recognition of memes is much more complicated than that of social text since it involves visual cues and language understanding. To address this issue, this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the fea- tures from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released re- sults show that our system performs better than the baseline algorithm. Our team won nine- teenth place in subtask A (Sentiment Classification). The code of this paper is availabled at : https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis. 1 Introduction In recent years, memes that combine pictures and text have been widely used in social media. Using memes can help users to express richer meaning and emotion compared with using text or images alone; hence, it is worthwhile to analyze the sentiment expressions of memes.Moreover, recognizing and analyzing the meaning and sentiment of memes is much more difficult than analyzing social texts or pictures. In SemEval-2020 Task 8:Memotion Analysis (Sharma et al., 2020), the organizers hoped that the task would increase the research attention given to the topic. The task is divided into three subtasks. • Task A- Sentiment Classification: Given an Internet meme, the first task is to classify its sentiment polarity. • Task B- Humor Classification: Given an Internet meme, the system has to identify the type of humor expressed. • Task C- Scales of Semantic Classes: The third task is to quantify the extent to which a particular effect is being expressed. Memes and this issue have attracted the attention of researchers. In a previous study, Borth (2013) pioneered the sentiment analysis of visual content with SentiBank. Another study implemented Optical Character Recognition (OCR) to extract the text captions of memes and then classified the sentiment polarity of the text using the Naive Bayes algorithm (Amalia et al., 2018). For a similar meme sentiment analysis task, Zhao (2019) developed a multimodal sentiment analysis method for image-text posts, and their experiments showed that this method achieves excellent performance on the Flickr benchmark dataset. Hu and Flaxman (2018) used GloVe to map the text to a high dimensional space and fine-tuned the …

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