| Challenge: | Emojis are able to express various linguistic components, such as emotions, sentiments, events, etc. emojis have the merit of preserving information more densely, compared to words, argues a new study. |
| Approach: | They propose to use passage-level and aspect-level emoji annotations to predict the proper emmojis associated with text. |
| Outcome: | The proposed method is heuristically generated and validated with a pre-trained BERT model. |
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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. |
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. |
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. |
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Multimodal Emoji Prediction (N18-2)
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| Challenge: | Emojis are small images that are commonly included in social media text messages. |
| Approach: | They propose a multimodal approach that is able to predict emojis in Instagram posts by using both text and image. |
| Outcome: | The proposed model incorporates both text and image to improve accuracy . |
The ELCo Dataset: Bridging Emoji and Lexical Composition (2024.lrec-main)
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| Challenge: | Emoji-Lexical Composition dataset provides parallel annotations of emoji sequences corresponding to English phrases. |
| Approach: | They propose a dataset that offers parallel annotations of emoji sequences corresponding to English phrases. |
| Outcome: | The Emoji-Lexical Composition (ELCo) dataset offers parallel annotations of emoji sequences corresponding to English phrases. |
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 . |
| Outcome: | The proposed model achieved an F-score of 0.7 for emoji use in 2475 tweets pairs. |
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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Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (C18-1)
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| Challenge: | Existing approaches focus on text information, but authors and overall ratings are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects. |
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MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification (L18-1)
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| Challenge: | sentiment analysis research has focused on unsupervised or semi-supervised approaches, but these still require a large number of resources and do not reach the performance of supervised approaches. |
| Approach: | They propose two datasets for supervised aspect-level sentiment analysis in Basque and Catalan. |
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Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms (2026.findings-eacl)
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| Challenge: | Recent work has explored the use of personal information in the form of persona sentences to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. |
| Approach: | They categorize self-disclosures and use them to build annotator models for predicting judgments of social norms by analyzing comments from original post. |
| Outcome: | The proposed model improves the model and its ability to predict annotator labels. |