In recent years, the use of emojis in social media has increased dramatically, making them an important element in understanding online communication. However, predicting the meaning of emojis in a given text is a challenging task due to their ambiguous nature. In this study, we propose a transformer-based approach for emoji prediction using BERT, a widely-used pre-trained language model. We fine-tuned BERT on a large corpus of text containing both text and emojis to predict the most appropriate emoji for a given text. Our experimental results demonstrate that our approach outperforms several state-of-the-art models in predicting emojis with an accuracy of over 75 percent. This work has potential applications in natural language processing, sentiment analysis, and social media marketing.

翻译：近年来，社交媒体中表情符号的使用量急剧增加，使其成为理解在线交流的重要元素。然而，由于表情符号的歧义性，在给定文本中预测其含义是一项具有挑战性的任务。在本研究中，我们提出了一种基于Transformer的方法，利用广泛使用的预训练语言模型BERT进行表情符号预测。我们在包含文本和表情符号的大规模语料库上对BERT进行微调，以预测给定文本中最合适的表情符号。实验结果表明，我们的方法在预测表情符号方面优于多个最先进模型，准确率超过75%。本工作潜在应用于自然语言处理、情感分析和社交媒体营销领域。