Understanding the Sarcastic Nature of Emojis with S arc O ji (2022)

Abstract: Identifying sarcasm is a challenging research problem owing to its highly contextual nature. Several researchers have attempted numerous mechanisms to incorporate context, linguistic aspects, and supervised and semi-supervised techniques to determine sarcasm. It has also been noted that emojis in a text may also hold key indicators of sarcasm. However, the availability of sarcasm datasets with emojis is scarce. This makes it challenging to effectively study the sarcastic nature of emojis. In this work, we present SarcOji which has been compiled from five publicly available sarcasm datasets. SarcOji contains labeled English texts which all have emojis. We also analyze SarcOji to determine if there is an incongruence in the polarity of text and emojis used therein. Further, emojis’ usage, occurrences, and positions in the context of sarcasm are also studied in this compiled dataset. With SarcOji we have been able to demonstrate that frequency of occurrence of an emoji and its position are strong indicators of sarcasm. SarcOji dataset is now publicly available with several derived features like sentiment scores of text and emojis, most frequent emoji, and its position in the text. Compilation of the SarcOji dataset is an initial step to enable the study of the role of emojis in communicating sarcasm. SarcOji dataset can also serve as a go-to dataset for various emoji-based sarcasm detection techniques.

## introduction sarcasm detection has piqued significant interest in various research communities, be it linguistics, psychology, or computational. identifying sarcasm requires context and background which become a challenge for computational models @xcite . @xcite identify three approaches to sarcasm detection, viz., rule-based, statistical (feature and learning-based), and deep-learning approaches. they also identify issues with these approaches. for instance, if we deal with the senti-ment (read polarity) as a feature, it may mislead a classifier because the surface sentiment might be different from the intent. they also note that in general sarcasm datasets are skewed in favor of non-sarcastic sentences. @xcite talk about explicit incongruity where words of both positive and negative polarities are present in a sarcastic text. while implicit incongruity may be expressed through an implied sentiment. many researchers have incorporated context and exploited context incongruity for sarcasm detection tasks @xcite , @xcite , @xcite , @xcite , @xcite , @xcite . but we opine that context may not always be available in real-world scenarios. for instance, in @xcite it is noted that apart from text and context outside the target, other modalities, too, are important for sarcasm detection; especially when the research trend is to use deep learning networks in sarcasm detection tasks. such classifiers need features that can be extracted by exploring other modalities. @xcite discussed how interest in learning emoji embeddings and using emojis for sentiment classification has evolved in the past few years. this work also discussed the need to explore the role of emojis to uncover complex and nuanced expressions of sarcasm and irony. on the other hand, many works have attempted to incorporate mixed or opposite polarities in sentences to detect sarcasm, @xcite , @xcite . apart from lexical features, researchers are also attempting to explore other features like slang, emoticons, emojis, reviews, etc. for sarcasm detection @xcite . in this work we focus on emojis to understand if and how they contribute to expressing sarcasm. we set to answer the following questions. therefore we compile from various benchmark and emoji datasets to create a labeled sarcasm dataset -sarcoji. sarcoji has text records, all with emojis. these records are augmented with derived features like sentiment scores of text and emojis. sentiment analysis tools like sentiwordnet @xcite , @xcite , vader @xcite , textblob 1 , and emoji sentiment ranking @xcite are used to compute sentiment scores. we compute sentiment scores to extract text and emoji polarities. moreover, these numerical features may be useful in training machine or deep learning classifiers for sarcasm detection. we also capture the most frequent emoji in the text along with its frequency and position of occurrence for an in-depth analysis of emoji usage in sarcastic texts. the rest of the paper is organized into four sections. in related work, various experiments and studies on emojis and sarcasm are discussed. in the methodology section, we elaborate on the compilation of the sarcoji dataset from five publicly available sarcasm datasets. we also discuss our mechanism to determine incongruence between the sentiment of text and emojis and determine the position of the most frequent emoji in the text. in the subsequent section, we report our observations and inferences from the sarcoji dataset. in the last section, we conclude and identify directions for future work in utilizing emojis for sarcasm detection. ## related work emojis are now one of the preferred modalities in sentiment analysis tasks. there have been many resources that are publicly available for use to identify emoji sentiments and the sense in which emojis are used. but, these resources do not holistically capture the sarcastic nature of emojis. the emoji sentiment ranking (esr) by kralj novak et al. 1 https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf (2015) computes the sentiment of 751 popular emojis from the sentiment of the tweets where these emojis are used. in this work, it is also reported that the emojis with high sentiment scores (negative or positive) occur towards the end of the tweet and on average, an emoji occurs at a two-thirds length of a tweet. but esr does not capture the sentiment of the latest emojis which makes it difficult to fully utilize its strengths. a machine-readable emoji inventory linking emoji unicode representations with their english meanings is presented in emojinet (wijeratne et al., 2017a). this inventory contains different senses (noun, verb, adjective), etc. in which an emoji can be used. emojinet can be used in the disambiguation of emoji senses and identifying similarities between emojis. while this is a very powerful resource for emoji disambiguation, how these senses can be used to determine sarcasm is yet to be explored. wijeratne et al. (2017b) compiled the emosim508 dataset with similarity scores of 508 pairs of emojis. in this work, emojinet was used to extract word descriptions of emojis and learn emoji embeddings. several experiments have been carried out to observe emoji usage across social media and how can they be used to capture sarcasm. zhao et al. (2018) analyzed emoji usage on social media and observed that 70% of emojis occurred towards the end of the tweets, while only 2.6% are used at the beginning. thompson et al. (2016) conducted experiments with 51 participants and found that emoticons were used more in sarcastic texts. they also reported that tongue and wink face are strong indicators of sarcasm. garcia et al. (2022) report that emojis can help both young and older adults discern sarcasm. miller et al. (2017) refute the previous hypothesis that emojis when placed with textual context may reduce miscommunication. they concur that surrounding text does not reduce emoji ambiguity and attribute this result to possible sarcasm. many researchers have conducted experiments to incorporate emojis in sarcasm classification tasks. felbo et al. (2017) built a large text corpus with emojis to learn emotional content, sarcasm, and sentiment detection in texts. they created a pretrained model called deepmoji. but the success of deepmoji heavily relied on tweets and their length. wang et al. (2021) used the speaker's prior probability of sarcasm and embedded emojis to recognize sarcasm. rustagi et al. (2022) integrate emojis, ratings, and reviews to enhance sarcasm classification tasks tewani (2019 ) uses polarity of texts, emojis, and hashtags to classify sarcasm on a small dataset of 650 tweets. there have been different approaches to understanding emoji usage, emoji sense, and incorporating emojis to detect sarcasm, but we are yet to come across a study that attempts to investigate whether popular emojis used with sarcasm or if they are indeed incongruent with the surrounding text or does their position or frequency matter when sarcasm is expressed? we now move ahead to describe the compilation and analysis of a dedicated sarcasm dataset with emojis -sarcoji. ## methodology in this section, we describe the various sources from which the sarcoji dataset was compiled. we further list down steps to mine frequent emojis used in the dataset and how sentiment scores of the text, as well as emojis, were computed using different tools. ## observations and inferences in this section, we discuss some important observations and inferences after an in-depth analysis of the sarcoji dataset. ## conclusion and future directions in this work, we compiled a labeled sarcasm dataset sarcoji from five publicly available datasets. sar-coji contains 29377 labeled records with emojis. sentiment scores were derived using sentiwordnet, vader, and textblob for both text and emojis, and emoji sentiment ranking was used to compute the sentiment score of maxemoji. using this publicly available dataset, researchers can explore the role of emojis in sarcasm detection. on studying sarcoji no significant incongruence between sarcastic text and corresponding emojis was found. but more exploration is needed to understand the role of seemingly neutral texts and emojis. it was also observed that sarcastic texts, as well as emojis used with them, are more positive as compared to non-sarcastic texts.