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Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis
Computation and Language · Submitted 27 Oct 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | With the explosive growth of social media, opinionated postings with emojis have increased explosively. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s1 | Many emojis are used to express emotions, attitudes, and opinions. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s2 | Emoji representation learning can be helpful to improve the performance of emoji-related natural language processing tasks, especially in text sentiment analysis. | background 1.00 | 0.07 | 0.08 | hedged possibility 0.86 |
| s3 | However, most studies have only utilized the fixed descriptions provided by the Unicode Consortium without consideration of actual usage scenarios. | gap 0.95 | 0.04 | 0.02 | asserted 1.00 |
| s4 | As for the sentiment analysis task, many researchers ignore the emotional impact of the interaction between text and emojis. | gap 0.95 | 0.04 | 0.02 | asserted 1.00 |
| s5 | It results that the emotional semantics of emojis cannot be fully explored. | gap 0.94 | 0.06 | 0.03 | asserted 1.00 |
| s6 | 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. | method 0.70 | 0.94 | 0.09 | asserted 0.98 |
| s7 | 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. | method 0.91 | 0.92 | 0.10 | asserted 0.97 |
| s8 | Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets. | result 1.00 | 0.96 | 0.76 | asserted 1.00 |
| s9 | Additionally, we conduct a series of ablation and comparison experiments to investigate the effectiveness and interpretability of our model. | contribution 0.53 | 0.96 | 0.05 | asserted 0.86 |
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The paper
Original abstract
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.
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