Papers with emotion prediction
Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text (N18-4)
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| Challenge: | Emotion Prediction is a natural language processing task dealing with detection and classification of emotions in monolingual and bilingual texts. |
| Approach: | They propose a machine learning system which uses various machine learning techniques to detect emotion associated with tweets. |
| Outcome: | The proposed system uses various machine learning techniques to detect emotion associated with the text. |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
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| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification (2024.naacl-short)
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Michail Mitsios, Georgios Vamvoukakis, Georgia Maniati, Nikolaos Ellinas, Georgios Dimitriou, Konstantinos Markopoulos, Panos Kakoulidis, Alexandra Vioni, Myrsini Christidou, Junkwang Oh, Gunu Jho, Inchul Hwang, Georgios Vardaxoglou, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis
| Challenge: | Emotion detection in textual data is pivotal for developing empathetic human-computer interaction systems. |
| Approach: | They propose a method for categorizing emotions from textual data that acknowledges similarities and distinctions of various emotions. |
| Outcome: | The proposed method preserves high accuracy in emotion prediction and significantly reduces errors in misclassification cases. |
ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts (2026.eacl-long)
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| Challenge: | Recent advances in NLP have greatly improved outcomes in emotion prediction and harmful content detection. |
| Approach: | They propose to classify Vietnamese comments into 27 distinct emotions using a model-based lexical normalization system and a transformer-based model. |
| Outcome: | The proposed corpus of 20,664 social media comments is based on a novel model that can support multiple architectures, but its quality and preprocessing strategies remain key factors influencing performance. |
The Correlation Between Emotion in Text and Speech Segments is Limited: A Cross-Modal Study (2026.findings-eacl)
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| Challenge: | a recent study has shown that text-to-speech systems can capture human-like emotion, but they lack the ability to predict emotion in speech. |
| Approach: | They propose to use 8 large language models for identifying emotion in text and 2 audio models for emotion in speech to investigate the correlation between emotion and speech. |
| Outcome: | The proposed models perform well on emotion recognition from situational text and audiobooks, but show weak correlation for Valence only. |
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (N18-1)
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| Challenge: | a recent shift towards expressive emotion representation models has hampered deep learning in sentiment analysis. |
| Approach: | They propose a multi-task learning problem to solve a language data bottleneck . they propose to use word emotion induction as an individual task to predict emotion . |
| Outcome: | The proposed model outperforms a wide range of other methods on 9 languages and 15 conditions. |
Modelling the interplay of metaphor and emotion through multitask learning (D19-1)
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| Challenge: | Existing research suggests that metaphorical phrases are more emotionally evocative than their literal counterparts. |
| Approach: | They propose a joint model of the relationship between metaphor and emotion within a computational framework by using hard and soft parameter sharing. |
| Outcome: | The proposed model advances the state of the art in both of these tasks. |
Analyzing Key Factors Influencing Emotion Prediction Performance of VLLMs in Conversational Contexts (2024.emnlp-main)
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| Challenge: | Recent studies show that large language models and vision large language model (VLLMs) possess EI and the ability to understand emotional stimuli in the form of text and images. |
| Approach: | They analyze the key elements affecting the emotion prediction performance of VLLMs in conversational contexts. |
| Outcome: | The proposed model performance was compared with other models in a conversational context. |
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs fail to capture the nuances of human emotions, making their interactions seem impersonal or inadequate. |
| Approach: | They propose a two-stage automatic data generation framework to generate a Chinese dataset called CAPE . their data is a cognitive appraisal theory-based Emotional corpus that accounts for personal and situational factors. |
| Outcome: | The proposed framework can generate human-like responses in conversation with large language models. |
Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction (2020.coling-main)
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| Challenge: | Existing work on emotion prediction for resource-rich languages has focused on code-mixed social media corpus but not on Kannada-English code-mixed Twitter data. |
| Approach: | They analyze Kannada-English code-mixed Twitter corpus annotated with their respective ‘Emotion’ for each tweet. |
| Outcome: | The proposed model based on Kannada-English code-mixed Twitter corpus yielded an accuracy of 30% and 32% respectively. |
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)
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| Challenge: | Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes. |
| Approach: | They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence. |
| Outcome: | The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset. |
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)
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| Challenge: | Existing models for speech emotion recognition are not suitable for emotional tasks. |
| Approach: | They propose a universal speech emotion representation model that is pre-trained on open-source emotion data. |
| Outcome: | euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets . |