Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter (2021.acl-short)
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| Challenge: | Existing methods for labeling emotions in text are limited, but they can be useful for many tasks. |
| Approach: | They propose a method to collect texts with induced emotion and induced sentiment labels. |
| Outcome: | The proposed method can augment the data with induced emotion and induced sentiment labels. |
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Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
| Outcome: | The proposed dataset is useful for training and testing supervised machine learning algorithms . it is based on the results of the SemEval-2018 task 1: Affect in Tweets . |
CHEER-Ekman: Fine-grained Embodied Emotion Classification (2025.acl-short)
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| Challenge: | Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. |
| Approach: | They propose to extend existing binary embodied emotion dataset with Ekman’s six basic emotion categories. |
| Outcome: | The proposed dataset outperforms existing methods with large language models. |
GoEmotions: A Dataset of Fine-Grained Emotions (2020.acl-main)
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| Challenge: | Existing datasets for language-based emotion classification are limited and small . existing datasets lack quality annotations for many different emotion categories . |
| Approach: | They propose to use a large manually annotated dataset to study emotion expressions . they conduct transfer learning experiments with existing emotion benchmarks to test their model . |
| Outcome: | The proposed model achieves an average F1-score of .46, leaving room for improvement. |
An Individualized News Affective Response Dataset (2024.acl-srw)
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| Challenge: | a new dataset captures subjective affective responses to news headlines . current methods to assess emotion detection ignore subjective differences in groups and individuals . |
| Approach: | They propose a large-scale dataset capturing subjective affective responses to news headlines . the dataset includes Facebook post screenshots from popular UK media outlets . |
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MojiTalk: Generating Emotional Responses at Scale (P18-1)
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| Challenge: | Existing studies on emotion-generating systems focus on small sets of labeled datasets. |
| Approach: | They propose to leverage Twitter data that are naturally labeled with emojis to generate emotional responses. |
| Outcome: | The proposed models can generate high-quality conversation responses in accordance with designated emotions. |
Emotion analysis and detection during COVID-19 (2022.lrec-1)
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| Challenge: | 3,000 English tweets labeled with emotions are used to predict emotions during crises . authors propose semi-supervised learning to bridge this gap . |
| Approach: | They propose to use a dataset of 3,000 English tweets labeled with emotions . they propose semi-supervised learning to bridge this gap by analyzing unlabeled data . |
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RESEMO: A Benchmark Chinese Dataset for Studying Responsive Emotion from Social Media Content (2024.findings-acl)
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| Challenge: | Existing studies on social media text processing do not focus on responsive emotion analysis. |
| Approach: | They propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China. |
| Outcome: | The proposed dataset includes 3813 posts with 68,781 comments collected from weibo, the largest social media platform in China. |
My Heart Skipped a Beat! Recognizing Expressions of Embodied Emotion in Natural Language (2024.naacl-long)
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| Challenge: | a new task is needed to recognize physical manifestations of emotions in natural language . physical manifestation of emotions affects not only our mental state but also our physical state . |
| Approach: | They propose a task to recognize expressions of embodied emotion in natural language . they use body part mentions with human annotations to extract emotional manner expressions . |
| Outcome: | The proposed model can train without gold data and improve performance with gold data. |
Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection (2023.findings-emnlp)
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| Challenge: | a negative emotion is a cognitive bias that affects how we express thoughts and opinions online . a recent study shows that negative words generate more engagement and clicks than positive ones . |
| Approach: | They propose to use readability and linguistic complexity metrics to better understand emotions . they propose to fine-tune three state-of-the-art transformers to detect emotions based on a dataset . |
| Outcome: | The proposed model fails to predict emotions on complex texts, the authors show . they also show that more advanced models fail to predict complex texts . |
Distribution of Emotional Reactions to News Articles in Twitter (L18-1)
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| Challenge: | Social networks have created datasets of opinions of users that focus on the writers' perspective, which does not consider the source that provokes those opinions. |
| Approach: | They propose to analyze opinions of Twitter users' after reading a news article and use it to predict the distribution of emotions. |
| Outcome: | The proposed dataset aims to explore how the six emotions are expressed by Twitter users' after reading a news article. |