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With the explosive growth of social media, opinionated postings with emojishave increased explosively. Many emojis are used to express emotions,attitudes, and opinions. Emoji representation learning can be helpful toimprove the performance of emoji-related natural language processing tasks,especially in text sentiment analysis. However, most studies have only utilizedthe fixed descriptions provided by the Unicode Consortium without considerationof actual usage scenarios. As for the sentiment analysis task, many researchersignore the emotional impact of the interaction between text and emojis. Itresults that the emotional semantics of emojis cannot be fully explored. Inthis work, we propose a method called EmoGraph2vec to learn emojirepresentations by constructing a co-occurrence graph network from social dataand enriching the semantic information based on an external knowledge baseEmojiNet to embed emoji nodes. Based on EmoGraph2vec model, we design a novelneural network to incorporate text and emoji information into sentimentanalysis, which uses a hybrid-attention module combined with TextCNN-basedclassifier to improve performance. Experimental results show that the proposedmodel can outperform several baselines for sentiment analysis on benchmarkdatasets. Additionally, we conduct a series of ablation and comparisonexperiments to investigate the effectiveness and interpretability of our model.

Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis