Sentiment-Aware Emoji Insertion Via Sequence Tagging

Fuqiang Lin, Yiping Song, Xingkong Ma, Erxue Min, Bo Liu

Abstract: 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.