Fusion Pre-trained Emoji Feature Enhancement for Sentiment Analysis

Emoji are often used in social media to enrich users’ emotions, and they play an important role in the task of social media sentiment analysis. In practice, researchers are more likely to consider emoji as special symbols and treat them separately from the text. Some existing methods use emoji as a dictionary for matching or converting emoji into text. However, these methods disregard the relationship between emoji and context, blue and they do not reflect the emotions that users are expected to express. It is challenging to incorporate the original emotions of emoji in social media sentiment analysis. In this paper, we propose the EPE model: Emoji Pre-trained feature Enhanced sentiment analysis. Specifically, we collected 8 million tweets and selected 5 million tweets with pre-trained emoji with context using the BERT model. We labeled 20,000 tweets as a three-category dataset and used Bi-LSTM with an attention layer to extract text features. Emoji were retained as key emotion information and combined with text features in the final layer as a connected vector for final prediction. Experimental results with our dataset showed that the proposed EPE model achieved better performance than other baseline models.