| 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. |
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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. |
Classifying the Informative Behaviour of Emoji in Microblogs (L18-1)
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| 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 . |
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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. |
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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. |
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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. |
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. |
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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 . |
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The Prosody of Emojis (2026.acl-long)
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| Challenge: | emojis are useful in spoken communication because they add affective and pragmatic nuance to textual cues. |
| Approach: | They analyze human speech data to find prosodic features that are important in spoken communication. |
| Outcome: | The proposed model shows that speakers adapt prosody based on emoji cues, and that listeners can recover intended meanings significantly above chance. |
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)
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| Challenge: | Modern NLP systems are typically ill-equipped when applied to noisy user-generated text. |
| Approach: | They propose a new evaluation framework consisting of seven Twitter-specific classification tasks. |
| Outcome: | The proposed framework is based on seven heterogeneous Twitter-specific classification tasks. |
Twitter Universal Dependency Parsing for African-American and Mainstream American English (P18-1)
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| Challenge: | We analyze the performance disparities between AAE and Mainstream American English (MAE) because of Twitter-specific conventions and dialectal language. |
| Approach: | They develop a dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework and annotate it. |
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