Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this article, we investigate a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work. First, it does not need feature engineering. Second, it utilizes the attention approach to model the impact of emojis on text. An evaluation on 10,042 manually labeled Sina Weibo showed that our model achieves much better performance compared with several strong baselines. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/emoji.

中文翻译：

---

使用注意网络的基于表情符号的情感分析

表情符号经常用于在社交媒体中表达情绪、情绪和感受。对表情符号和情绪有很多研究。然而，现有方法主要面临两个限制。首先，他们将表情符号视为二元指标特征，并依靠手工制作的特征进行基于表情符号的情绪分析。其次，他们将表情符号和文本的情感分开考虑，没有充分探索表情符号对文本情感极性的影响。在本文中，我们研究了一种基于双向长短期记忆的情感分析模型，该模型与现有工作相比具有两个优点。首先，它不需要特征工程。其次，它利用注意力方法来模拟表情符号对文本的影响。10日的评价，042 手动标记的新浪微博表明，与几个强基线相比，我们的模型实现了更好的性能。为方便相关研究，我们的语料库将在 https://github.com/yx100/emoji 上公开。