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

arXiv: https://arxiv.org/abs/2110.14636 · HTML: https://mlsanity.com/paper/2110.14636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [emoji2vec: Learning Emoji Representations from their Description](https://mlsanity.com/paper/1609.08359/) (2016)
- [Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji](https://mlsanity.com/paper/1703.02860/) (2017)
- [EmojiNet: An Open Service and API for Emoji Sense Discovery](https://mlsanity.com/paper/1707.04652/) (2017)
- [A Semantics-Based Measure of Emoji Similarity](https://mlsanity.com/paper/1707.04653/) (2017)
- [Learning Emoji Embeddings using Emoji Co-occurrence Network Graph](https://mlsanity.com/paper/1806.07785/) (2018)

## Abstract (arXiv)

With the explosive growth of social media, opinionated postings with emojis have increased explosively. Many emojis are used to express emotions, attitudes, and opinions. Emoji representation learning can be helpful to improve the performance of emoji-related natural language processing tasks, especially in text sentiment analysis. However, most studies have only utilized the fixed descriptions provided by the Unicode Consortium without consideration of actual usage scenarios. As for the sentiment analysis task, many researchers 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 work, we propose a method called EmoGraph2vec to learn emoji representations by constructing a co-occurrence graph network from social data and enriching the semantic information based on an external knowledge base EmojiNet to embed emoji nodes. Based on EmoGraph2vec model, we design a novel neural network to incorporate text and emoji information into sentiment analysis, which uses a hybrid-attention module combined with TextCNN-based classifier to improve performance. Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets. Additionally, we conduct a series of ablation and comparison experiments to investigate the effectiveness and interpretability of our model.