Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1141–1147 Barcelona, Spain (Online), December 12, 2020. 1141 Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification Guillermo Ruiz* and Eric S. Tellez** and Daniela Moctezuma* and Sabino Miranda-Jim´enez** and Tania Ram´ırez-delReal* and Mario Graff** *CONACyT - CentroGEO, Aguascalientes, M´exico **CONACyT - INFOTEC, Aguascalientes, M´exico {lgruiz,dmoctezuma,tramirez}@centrogeo.edu.mx {eric.tellez,sabino.miranda,mario.graff}@infotec.mx Abstract The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification. 1 Introduction The kind of information usually shared on social media can be in various modalities such as text, images, audio, or video. In spite that this kind of information is shared in a recreational way, many institutions have paid attention to what the users or citizens shared on the Internet. An aspect that has been analyzed in the past is the polarity of the text (for instance tweets, reviews, news). The polarity reflects if the text has negative, positive, or neutral content. More recently, other kinds of data have gain attention, for instance, images. The most popular and descriptive mix of image and text are the memes. Richard Dawkins proposed the term meme referring to mimeme in Greek, that is associated with imitation (Dawkins, 2016). He expressed that a meme is a form of social engendering or cultural propagation. Memes are important because they express emotion, joke, humor, or something that only using words could not be possible. The automatic classification of memes has very important challenges. The meaning of a meme could be interpreted in different ways by different people. Furthermore, for analyzing and classifying the meaning of a meme, image and text processing must be tackled and combining them is the best way to reach a good classification result (Iwazaki, 2018; Xia et al., 2020), which is not precisely easy to achieve. Some works have tackled the meme classification problem. Kumar and Garg, (2019) proposed a method for detecting only sarcasm tone in memes. For the sarcastic detection, a set of typo-graphic only memes where used. The authors extracted the text from the images and used standard TF-IDF approach over the words. Finally, they reported a result of an accuracy of 88% with a multilayer perceptron classifier from a dataset called MemeBank obtained from the Instagram social network using the hashtags #sarcasm, #sarcastic and #irony as positive examples and #motivational and #inspirational as negative. Smitha et al., (2018); Peirsoon and Tolunay, (2018), and Kanai, (2016) dealt …

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