This paper delves into how social media, an increasingly pervasive global phenomenon, shapes the language nowadays. It zeroes in on emojis, a rapidly evolving means of communication that deviates from traditional verbal and nonverbal expressions, examining their impact on linguistic development. Emojis, initially conveying emotions, have shifted towards representing feelings rather than explicit meanings. Users now create entire messages using strings of emojis instead of sentences or phrases. The present study examines how people understand emoji-based messages, particularly through a survey conducted in Saudi Arabia. Additionally, an intelligent system leveraging machine learning is introduced to decode the meanings within emoji messages. The creation of EmojiString, a novel dataset, aids in better understanding these messages by utilizing advanced models like long short-term memory (LSTM) and MultiLayer Perceptron (MLP). The proposed model boasts an average accuracy of 82.22%, surpassing the existing methods. These results strongly support the idea that emojis serve as vital contextual cues in everyday communication. They are not just whimsical symbols, but meaningful elements that shape the interactions between persons. This research underscores the need to recognize emojis’ nuanced roles in the evolution of modern language, marking a significant step forward in understanding their impact on how people communicate with one another.
Emojis, WhatsApp, Natural language, Computer-mediated discourse, Machine learning, LSTM, MLP.
Mossaad BEN AYED, Ali ALSAAWI, "A Novel Machine Learning Model for Predicting the Meaning of an Emojis String in Social Media Platforms", Studies in Informatics and Control, ISSN 1220-1766, vol. 33(1), pp. 91-98, 2024. https://doi.org/10.24846/v33i1y202408
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