Impact of Emojis on Automatic Analysis of Individual Emotion Categories (2023)

Abstract: Automatic emotion analysis is a highly challenging task for Natural Language Processing, which has so far mainly relied on textual contents to determine the emotion of text. However, words are not the only media that carry emotional information. In social media, people also use emojis to convey their feelings. Recently, researchers have studied emotional aspects of emojis, and use emoji information to improve the emotion detection and classification, but many issues remain to be addressed. In this study, we examine the impact of emoji embedding on emotion classification and intensity prediction on four individual emotion categories, including anger, fear, joy, and sadness, in order to investigate how emojis affect the automatic analysis of individual emotion categories and intensity. We conducted a comparative study by testing five machine learning models with and without emoji embeddings involved. Our experiment demonstrates that emojis have varying impact on different emotion categories, and there is potential that emojis can be used to enhance emotion information processing.

## introduction in this study, we investigate the issue of how emojis can impact on the automatic analysis of emotion in social media messages. this topic has been studied over past years, but further research is needed to fully understand the characteristics of the emojis and how they contribute to the conveyance of emotion. automatic emotion analysis is a process of identifying emotions expressed by people. in social media, the emotions can be conveyed with various media including text, emojis, pictures, or other codes. because social media platforms impose little or no restriction on language usage in terms of grammar and formality, social media data contains a wide range of styles and forms, including informal, colloquial, slang, and ungrammatical expressions, mixed with emojis and other images. such an unconstrained writing styles of social media messages present a tough challenge to the task of automatic emotion processing. as @xcite pointed out, the casual style and semantic ambiguity of social media messages are the main two challenges in determining emotions in such data. to improve the automatic emotion analysis, researchers started to consider emojis as additional features. for example, word and emoji embedding are combined in the hope to generate better features for emotion classification. emojis can contain emotion information that can help to identify emotions. however, as @xcite found, emojis are not always a good choice for representing emotion. in this work, firstly we carried out experiments of emotion classification of four emotion categories and emotion intensity prediction using word embeddings as the sole features based on emoint dataset @xcite , and used the results as a benchmark. then, we added emoji embeddings to the word embeddings to observe how the emoji information affects the performance of the emotion analysis. our experiment results show that, overall, adding emoji embedding can marginally improve emotion analysis for some emotion categories. we foresee that emoji embedding can potentially improve the performance of emotion analysis further if we can design better methods of combining word and emoji embeddings. ## related work recently, emojis have been used in automatic emotion analysis. for example, @xcite grouped commonly used emojis into six emotion categories, including anger, disgust, fear, happi-ness, sadness, and surprise. these emojis were used as emotion labels of messages for training emotion classification models. they also created a test data by manually annotating data. their emotion classifiers trained on the emoji-labeled dataset produced a good performance on joy and sadness, but produced slightly lower performance on the other emotion categories. another application of emojis is to use them to train better word embeddings @xcite to achieve a better emotion representation. the authors extracted a new word embedding by using @xcite 's word2vec model as an intermediate representation. firstly, they collected twitter data to train a word2vec model. then they created a new embedding model based on cosine similarity between words and emojis. they tested emotion intensity prediction by comparing emotag with well-known embedding models as benchmark, such as glove, and found emotag produced similar performances to that of the benchmark. @xcite developed an emoji embedding model named emoji2vec, which was trained on emoji names and keyword phrases from the unicode emoji list. they used google news word2vec embeddings to formulate vectors and represent emojis from their describing phrases to train emoji2vec. sentiment analysis task was used to evaluate the capability of emoji2vec, and the result showed that emoji2vec improves the overall performance of sentiment analysis. @xcite proposed another pretrained emoji embedding named fuzzymoji2vec. they compiled a list of commonly used emojis. then these emojis were classified into one or more emotion classes based on the correlation between emojis and emotion labels. the embedding was trained on emojis and their emotion labels. because the number of emojis in their dataset was limited, they extended the coverage of emojis using fuzzy clustering to classify unseen emojis collected from twitter. the unseen emojis were clustered based on messages classified into 11 emotions. fuzzy-moji2vec was reported to outperform emoji2vec in emotion classification. more recently, @xcite developed the pre-trained emoji embedding emojional. emojional learned emoji embedding based on keywords representing emojis collected from the online emoji dictionaries of emojipedia and emojiswiki. they employed google news word2vec to create input vectors. then they trained the embedding by predicting the corresponding emojis from the given inputs. they evaluated the emojional in comparison with fuzzymoji2vec and emoji2vec. they showed emojional was generally more accurate than state-of-the-art embeddings for the sentiment analysis task. the past research shows that emoji embedding can improve the performance of emotion analysis. however, the most past works mainly reported on overall performances. it is necessary to gain a deeper understanding of the characteristics of emojis and about how emoji embedding can affect analysis of individual emotion categories. this paper examines the impact of emoji embedding on emotion classification and intensity prediction on four emotion categories anger, fear, joy, and sadness, which are included in the emoint annotation. ## conclusion in this paper, we reported our study which aims to study the impact of emoji embeddings on emotion classification and intensity prediction in social media messages, using the emoint as our training and test dataset. we examined the performance of five machine learning models with all possible combinations between a set of three word embeddings (fasttext, glove, bert) and two emoji embeddings (emoji2vec and emojional). we compared the results obtained with and without emoji embeddings to assess the impact of emoji embedding on analysing individual emotion categories. because the emoint dataset only contains annotation of four emotion categories (joy, anger, fear and sadness), our study focused on these categories. in our experiment, we tested 18 different combinations of {classifier + word embedding + emoji embedding}. we observed improvement on emotion classification for fear in six cases, for joy in five cases, and anger and sadness in four cases. as for emotion intensity prediction, the improvements was observed for fear in eight cases, sadness in seven cases, anger in four cases, and joy in one case. therefore, it is a mixed picture how emojis can improve the automatic emotion analysis. we acknowledge our results are not conclusive, as we used simple embedding combination methods, and only a small portion of tweets in emoint contain emojis, making it difficult to examine the impact of emoji embeddings in further details. for future work, we aim to explore larger emoji embedding datasets and more embedding combination techniques.