BERT at S em E val-2020 Task 8: Using BERT to Analyse Meme Emotions (2020)

Abstract: Sentiment analysis, being one of the most sought after research problems within Natural Language Processing (NLP) researchers. The range of problems being addressed by sentiment analysis is increasing. Till now, most of the research focuses on predicting sentiment, or sentiment categories like sarcasm, humor, offense and motivation on text data. But, there is very limited research that is focusing on predicting or analyzing the sentiment of internet memes. We try to address this problem as part of “Task 8 of SemEval 2020: Memotion Analysis”. We have participated in all the three tasks under Memotion Analysis. Our system built using state-of-the-art Transformer-based pre-trained Bidirectional Encoder Representations from Transformers (BERT) performed better compared to baseline models for the two tasks A and C and performed close to the baseline model for task B. In this paper, we present the data used, steps used by us for data cleaning and preparation, the fine-tuning process for BERT based model and finally predict the sentiment or sentiment categories. We found that the sequence models like Long Short Term Memory(LSTM) and its variants performed below par in predicting the sentiments. We also performed a comparative analysis with other Transformer based models like DistilBERT and XLNet.

## introduction social media @xcite has gained a lot of traction since it's inception. almost 50% of the world's population is on social media. be it facebook, instagram, twitter or any other social media platform, content generation is growing exponentially day by day. recently, there has been a surge of using memes 1 as a communication medium in social media platforms. memes can take many forms; the most prominent usage of memes consisted of images combined with text. memes, being images, can have text in one or more languages. this makes it even more difficult for an algorithm to understand and decode its sentiment or any other characteristic. of late, smaller videos as memes are also gaining popularity. irrespective of the type of meme, the meme may get changed, remixed or recreated while communicating through social media networks @xcite . memes are used in contexts involving political discussion @xcite , to add a sarcastic perspective to the discussion, for social purposes, etc. meticulously analysing the memes can help us understand the involvement of societal factors like race and gender @xcite , implications on culture and the values promoted by the memes. moreover, analysing the meme's underlying emotion can help us understand and possibly eradicate propagation of fake news, offensive content through memes (earlier identification of offensive content is only based on text @xcite ) and might also help in the prevention of malicious content. image memes are also used to understand race and gender discourse on social media platforms, 4chan and reddit @xcite . memes, in future, might become an integral part of most of the people. understanding the meme emotions will help us understand societal transformation over years or even decades. ## related work recently, both natural language processing and vision & image processing research communities have been looking memes as a source of potential research. the remainder of this section talks about works related to meme analysis. the type of meme used in the communication is directly correlated to the nature of the topic in the social media and it is demonstrated that memes highlight the semantic context of the discussions on social media platforms @xcite . memes are used in analysing serious social issues such as homophobic bullying of lesbian, gay, bisexual, transgender and queer (lgbtq) community and establish collective identity @xcite . another area of focus is to understand the reasons for the spread of memes (or any content). some of the reasons are novelty, simplicity, coherence and proselytism (a condition in which the meme provokes the other users to spread it further) @xcite . five other reasons for meme participation in social media are spread, emotional attachment to the users, ability to spread through different channels of communication, ability to add new meanings to existing image or text and provocation of other users to spread the meme @xcite . though there is a lot of research happening around internet memes, there is very little attention towards analysing meme emotions. hu and flaxman (2018) built a multi-modal deep neural network architecture to infer the emotional state of the user and predict the emotion word tags attached by the users to their tumblr posts. however, this work does not focus on identifying the sentiment or variants of the sentiment like humour, sarcasm and motivation. this is our target area of research in this paper. the structure of the paper is as follows. in section 3, we give an overview of the tasks and dataset used for the experiment. section 4 describes the experimental setup which includes data pre-processing, model description and training strategies, while section 5 discusses the experimented results of various models. finally, we conclude with concluding remarks and future direction of research in section 6. ## tasks and dataset description the data provided by the organizers has 6601 and 1878 samples for train data and test data respectively. the data need some cleansing process as training text is missing in some samples, labels are missing in some other. class-wise number of samples in the training data for all the tasks after cleaning are shown in the