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Microblogs have become a social platform for people to express their emotionsin real-time, and it is a trend to analyze user emotional tendencies from theinformation on Microblogs. The dynamic features of emojis can affect thesentiment polarity of microblog texts. Since existing models seldom considerthe diversity of emoji sentiment polarity,the paper propose a microblogsentiment classification model based on ALBERT-FAET. We obtain text embeddingvia ALBERT pretraining model and learn the inter-emoji embedding with anattention-based LSTM network. In addition, a fine-grained attention mechanismis proposed to capture the word-level interactions between plain text andemoji. Finally, we concatenate these features and feed them into a CNNclassifier to predict the sentiment labels of the microblogs. To verify theeffectiveness of the model and the fine-grained attention network, we conductcomparison experiments and ablation experiments. The comparison experimentsshow that the model outperforms previous methods in three evaluation indicators(accuracy, precision, and recall) and the model can significantly improvesentiment classification. The ablation experiments show that compared withALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicatingthat the fine-grained attention network can understand the diversifiedinformation of emoticons.

Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments