Emoji-Aware Attention-based Bi-directional GRU Network Model for Chinese Sentiment Analysis

Nowadays, social media has become the essential part of our lives. Pictograms (emoticons/emojis) have been widely used in social media as a medium for visually expressing emotions. In this paper, we propose a emoji-aware attention-based GRU network model for sentiment analysis of Weibo which is the most popular Chinese social media platform. Firstly, we analyzed the usage of 67 emojis with facial expression. By performing a polarity annotation with a new “humorous type” added, we have confirmed that 23 emojis can be considered more as humorous than positive or negative. On this basis, we applied the emojis polarity to a attentionbased GRU network model for sentiment analysis of undersized labelled data. Our experimental results show that the proposed method can significantly improve the performance for predicting sentiment polarity on social media.

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