| Challenge: | Emoji are pictographs used in microblogs as emotion markers, but can also represent a wider range of concepts. |
| Approach: | They analyze a corpus of tweets pairs and classify emoji with respect to redundancy . they propose to further investigate the informative behaviour of e-mails using eoji . |
| Outcome: | The proposed model achieved an F-score of 0.7 for emoji use in 2475 tweets pairs. |
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EmoTag1200: Understanding the Association between Emojis and Emotions (2020.emnlp-main)
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| Challenge: | Emojis are increasingly used to convey affect, but their use is not trivial. |
| Approach: | They propose to use human-solicited association ratings to explore the connection between emojis and emotions to conduct experiments. |
| Outcome: | The proposed method can be inferred from word-level information when high-quality information is available. |
Assessing Emoji Use in Modern Text Processing Tools (2021.acl-long)
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| Challenge: | Emojis are textual elements that are encoded as characters but rendered as small digital images or icons that can be used to express an idea or emotion. |
| Approach: | They propose to use a set of popular NLP tools to assess the support of emojis in tweets. |
| Outcome: | The proposed methods show that many systems still have notable shortcomings when operating on text containing emojis. |
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
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| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
How to Do Things without Words: Modeling Semantic Drift of Emoji (2022.findings-emnlp)
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| Challenge: | Emoji have become a significant part of our informal textual communication. |
| Approach: | They propose to model and analyze the semantic drift of emoji and explore the relations between graphical changes and semantic changes. |
| Outcome: | The proposed model and analysis examines the relationship between graphical changes and semantic drift. |
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)
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| Challenge: | Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text. |
| Approach: | They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning. |
| Outcome: | The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks. |
Semantics and Sentiment: Cross-lingual Variations in Emoji Use (2024.emnlp-main)
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| Challenge: | emojis have been used in social media for a decade but have been inconsistently used in contexts and in isolation. |
| Approach: | They develop a corpus containing literal meanings for emojis defined by L1 speakers in three languages to assess their e-mail sentiments. |
| Outcome: | The proposed method shows that emoji semantics differ across languages and how it interacts with sentiment in e-mails. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
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| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
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| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
Exploiting Emojis for Abusive Language Detection (2021.eacl-main)
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| Challenge: | emojis can be used as a proxy for learning a lexicon of abusive words . eliot safina and samuel khan are the authors of this paper . |
| Approach: | They propose to use abusive emojis as a proxy for learning a lexicon of abusive words. |
| Outcome: | The proposed approach generates a lexicon that performs as well as the most advanced lexical induction method. |
Emoji-Based Transfer Learning for Sentiment Tasks (2021.eacl-srw)
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| Challenge: | Sentiment tasks such as hate speech detection and sentiment analysis are often low-resource . a transfer learning approach is used to transfer the emotional information encoded in emojis to a sentiment task . |
| Approach: | They exploit emotional information encoded in emojis to enhance performance on sentiment tasks . they use a transfer learning approach where parameters learned by an e-based source task are transferred to a sentiment target task . |
| Outcome: | The proposed method improves sentiment tasks on languages other than English with high emoji content and label distribution under three conditions. |