  Emojis are widely used in online social networks to express emotions,
attitudes, and opinions. As emotional-oriented characters, emojis can be
modeled as important features of emotions towards the recipient or subject for
sentiment analysis. However, existing methods mainly take emojis as heuristic
information that fails to resolve the problem of ambiguity noise. Recent
researches have utilized emojis as an independent input to classify text
sentiment but they ignore the emotional impact of the interaction between text
and emojis. It results that the emotional semantics of emojis cannot be fully
explored. In this paper, we propose an emoji-based co-attention network that
learns the mutual emotional semantics between text and emojis on microblogs.
Our model adopts the co-attention mechanism based on bidirectional long
short-term memory incorporating the text and emojis, and integrates a
squeeze-and-excitation block in a convolutional neural network classifier to
increase its sensitivity to emotional semantic features. Experimental results
show that the proposed method can significantly outperform several baselines
for sentiment analysis on short texts of social media.
