| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |
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Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
| Approach: | They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources. |
| Outcome: | The proposed method produces (near-)gold quality emotion lexicons even in crosslingual settings. |
Modeling Label Semantics for Predicting Emotional Reactions (2020.acl-main)
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| Challenge: | Existing methods for predicting how events induce emotions ignore the semantics of the labels themselves. |
| Approach: | They propose that the semantics of emotion labels can guide a model’s attention when representing the input story. |
| Outcome: | The proposed model can model the semantics of emotion labels and track correlations on unlabeled data. |
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 . |
An Analysis of Annotated Corpora for Emotion Classification in Text (C18-1)
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| Challenge: | Several datasets have been annotated and published for classification of emotions. |
| Approach: | They aggregated emotion corpora in a common file format with a shared annotation schema . they perform cross-corpus classification experiments to gain insight and a better understanding of differences . |
| Outcome: | The proposed model can be trained on a subset of corpora, but not on all corporata. |
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
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| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
| Approach: | They propose a semi-supervised learning approach that minimizes the distance between source and target instances in embedded feature space. |
| Outcome: | The proposed approach can improve on baseline methods in various settings. |
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)
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| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
| Approach: | They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels. |
| Outcome: | The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media . |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
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| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
Learning Emotion-enriched Word Representations (C18-1)
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| Challenge: | Existing word representations based on distributional hypothesis do not provide accurate representations of emotions. |
| Approach: | They propose a method to obtain emotion-enriched word representations by remote supervision using a large training dataset of text documents and two recurrent neural network architectures. |
| Outcome: | The proposed method outperforms competing general-purpose and affective representations on two tasks. |
Learning and Evaluating Emotion Lexicons for 91 Languages (2020.acl-main)
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |
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. |