Due to the booming popularity of online social networks, emojis have been widely used in online communication. As nonverbal language units, emojis help to convey emotions and express feelings. In this article, we focus on the sentiment-aware emoji insertion task, which predicts multiple emojis and their positions in a sentence conditioned on the plain texts and sentiment polarities. To facilitate future research in this field, we construct a large-scale emoji insertion corpus named “MultiEmoji,” which contains 420 000 English posts with at least one emoji per post. We formulate the insertion process as a sequence tagging task and apply a BERT-BiLSTM-CRF model to the insertion of emojis. Extensive experiments illustrate that our model outperforms existing methods by a large margin.

中文翻译：

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通过序列标记插入情感感知表情符号

由于在线社交网络的蓬勃发展，表情符号已广泛用于在线交流。作为非语言语言单元，表情符号有助于传达情感和表达感受。在本文中，我们专注于情感感知表情符号插入任务，该任务以纯文本和情感极性为条件预测多个表情符号及其在句子中的位置。为了促进该领域的未来研究，我们构建了一个名为“MultiEmoji”的大型表情符号插入语料库，其中包含 420 000 个英文帖子，每个帖子至少有一个表情符号。我们将插入过程制定为序列标记任务，并将 BERT-BiLSTM-CRF 模型应用于表情符号的插入。大量实验表明，我们的模型大大优于现有方法。