Making Your Tweets More Fancy: Emoji Insertion to Texts (2021)

Abstract: In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.

## introduction the advent of emojis has dramatically changed the style of human communication. currently, emojis are widely used on social media platforms such as twitter, facebook, and instagram. owing to the prevalent use of emojis in the last few years, they become a target of recent researches. as reported by @xcite , exploring the interplay between emojis and texts can be a clue for better natural language understanding (nlu) in social media datasets with full of emojis. in response to this report, @xcite proposed a task to predict an emoji label given a text, which can contribute to various natural language processing (nlp) tasks such as information retrieval, social media content generation, and sentiment/emotion analysis. to solve this task, they used an lstm-based tagger and achieved even higher performance than human prediction. however, in spite of the success of the previous researches, the existing emoji prediction task targets only at predicting a single emoji given a text without considering its position. kralj @xcite reported that social media users place an emoji differently based on its emoji type in a tweet. they analyzed 751 emoji labels by dividing them into positive, negative and neutral types to show that the type of an emoji strongly correlates with its position in a text. this indicates the information of the emoji position can be a good clue to boost emoji label prediction. hence, it is necessary to consider an emoji label and its position together. social media users do not simply put an emoji at the end of a sentence. automated systems of predicting an emoji with its position that consider surrounding linguistic information can further enhance the aforementioned tasks in the field of nlu and nlp. figure 1 describes the difference between the previous task of emoji prediction and our proposed task of emoji insertion to texts. in the first and second examples, we can understand that the position of emojis is important and indispensable in cases where they are used instead of a word or a character. in the third example, the position of an emoji is required to investigate the relationship between the emoji and multiple sentences. to take into account the position of emoji labels, we first demonstrate that the information of the emoji position can improve the performance of emoji label prediction by using the gold emoji position. we then extend the task of emoji prediction by proposing a novel task of inserting a suitable emoji into a suitable position in a given tweet. to solve this new method, we propose bi-affine-based and bi-lstm-based models that can jointly predict both emoji label and its position without relying on any linguistic features. class-imbalanced data is one of the major concerns in the field of emoji prediction @xcite because emojis of positive emotion are more frequently used on social media and dominate minor classes of negative and non-emotional emojis @xcite . it is difficult to obtain better results for imbalanced emoji classes. figure 2 shows the imbalanced frequencies of emojis that obey a power-law distribution. to deal with the imbalanced frequencies of emojis, we also propose a novel method, contextualized dynamic-smoothing (cds), which can be adopted in our proposed models. due to the problems of the original dataset for the shared task for multilingual emoji prediction in semeval2018 task2 @xcite , that sometimes mistakenly contains "traces" of emojis in tweets, we prepared the modified dataset for our experiments. experimental results show that incorporating the gold emoji position can improve the performance of emoji label prediction. in addition, our proposed models of jointly predicting an emoji label and its position outperform the baseline model for emoji label prediction that does not consider the emoji position in terms of f-1 scores. specifically, the results of our models with cds, that solves the problem of the imbalanced emoji frequency, show the additional improvement on infrequent and nonemotional emojis. human evaluation shows that there is a suitable emoji position in a tweet. in addition, our proposed task to insert an emoji in a tweet can actually make the tweet more fancy and natural, compared to the current emoji prediction task. considering emoji positions can further improve the performance on the irony detection task compared to the current emoji prediction. ## related work social media platforms contain various types of emotion expression methods such as emojis and kaomojis @xcite . recently, emojis occupy a large proportion on social media owing to the richness in their information. emoji information can enhance the quality of social media datasets for researches such as sentiment, irony, emotion, and sarcasm analysis @xcite . although emojis can be interpreted differently based on social media platforms @xcite , their usage is similar in multiple countries @xcite , meaning that they can be predicted in different languages such as english, spanish @xcite , and italian @xcite . emoji prediction can be boosted by incorporating not only texts but photos in the instagram dataset @xcite and can directly improve the task of sentiment analysis @xcite . also, emoji prediction can be used in dialogue systems to recommend a suitable emoji @xcite , and using emojis in chatbot systems is effective to attract users, specifically in the conversation for mental wellbeing @xcite . recently, @xcite released an emoji label prediction dataset with passage-level multi-class/multi-label, and aspect-level multi-class annotations. our work to predict both an emoji label and its position is in accordance with sequential labeling tasks. in dependency parsing, the bi-affine dependency label classifier was proposed to study the relationship between two words, and achieved the state-of-the-art performance @xcite . currently, bi-affine layers have been widely adopted in various tasks, such as relation extraction @xcite , mention detection and clustering @xcite , owing to their effectiveness. because they have demonstrated the standout performance in the aforementioned tasks, we employ them to learn emoji positions to predict emoji labels. ## models we introduce our proposed models for jointly predicting an emoji and its position in each tweet. bi-lstm networks are first used to encode a given tweet @xcite and a bi-affine layer is used to predict the position of an emoji. then, the predicted emoji position is used to predict the label of the emoji with cds. ## conclusion in this paper, we presented a novel task of jointly predicting the emoji label and its position by using contextualized dynamic-smoothing (cds) with bi-affine layers. the experimental results showed that the information of emoji positions is important and can improve the performance of emoji label prediction without external resources. because the original public datasets of emoji label prediction had problems, we prepared the modified datasets. human evaluation validated there exists a suitable emoji position in a tweet and our proposed task can make tweets more fancy and natural compared to the current emoji prediction. moreover, considering emoji position is effective on the task of irony detection.