Papers by Tiberiu Sosea
EnsyNet: A Dataset for Encouragement and Sympathy Detection (2022.lrec-1)
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| Challenge: | Existing studies on emotional support detection focus on the presence or absence of emotional support, while the available datasets are limited or scarce in terms of size. |
| Approach: | They propose to use a dataset of 6,500 sentences annotated with encouragement and sympathy to train BERT-based classifiers on this dataset and apply their best BERT model to two large scale experiments. |
| Outcome: | The proposed model improves the emotional state of users while the lack of emotional support negatively impacts patients’ emotional state. |
eMLM: A New Pre-training Objective for Emotion Related Tasks (2021.acl-short)
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| Challenge: | Emotion Masked Language Modelling improves the performance of a pretraining language model for emotion detection and sentiment analysis tasks. |
| Approach: | They propose a BERT-based version of Masked Language Modelling that induces emotion into the model. |
| Outcome: | The proposed model improves on emotion detection and sentiment analysis tasks by 1.2% F-1 . the proposed model also shows increased robustness in the test. |
Hard Emotion Test Evaluation Sets for Language Models (2025.findings-naacl)
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| Challenge: | Existing tests on emotion datasets do not show whether language models understand emotions or exploit supperficial lexical cues. |
| Approach: | They propose to use two existing emotion datasets to evaluate whether language models make inferential decisions for emotion detection. |
| Outcome: | The proposed test sets evaluate language models on emotion datasets. |
Multimodal Semi-supervised Learning for Disaster Tweet Classification (2022.coling-1)
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| Challenge: | During natural disasters, people use social media platforms to post information about casualties and damage . annotating data can be burdensome, subjective and expensive . et al., 2018b; sohn e.t., 2020) proposed semi-supervised multimodal approach to improve performance on multimodal tasks. |
| Approach: | They propose a semi-supervised approach to annotate unlabeled data from Twitter . they extend FixMatch algorithm to a multimodal setting to account for subjective data . |
| Outcome: | The proposed approach improves on multimodal disaster tweet classification tasks. |
Unsupervised Extractive Summarization of Emotion Triggers (2023.acl-long)
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| Challenge: | Recent approaches trained supervised models to detect emotions and explain emotion triggers via abstractive summarization, but this can block necessary responses. |
| Approach: | They propose to augment an abstractive dataset with extractive triggers and develop unsupervised models that can jointly detect emotions and summarize their triggers. |
| Outcome: | The proposed model outperforms existing models and is based on a COVID-19 crisis dataset. |
Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
Sarcasm Detection in a Disaster Context (2024.lrec-main)
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| Challenge: | During natural disasters, people often use social media platforms to express contempt or sarcasm . despite being widely researched as an NLP task, sarkasmatic detection has not been explored in a specific context . |
| Approach: | They propose a dataset of 15,000 tweets annotated for intended sarcasm . they propose sarkasmatic detection using pre-trained language models . |
| Outcome: | The proposed model can obtain as much as 0.70 F1 on the dataset. |
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 . |
| Outcome: | The proposed model can be used to predict emotions in the context of COVID-19 . the proposed model performs better than other models using unlabeled data . |
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
GunStance: Stance Detection for Gun Control and Gun Regulation (2024.acl-long)
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Nikesh Gyawali, Iustin Sirbu, Tiberiu Sosea, Sarthak Khanal, Doina Caragea, Traian Rebedea, Cornelia Caragea
| Challenge: | Social media, especially Twitter, has been a melting pot for such debates. |
| Approach: | They propose to annotate tweets relevant to shooting events into three classes: In-Favor, Against, and Neutral. |
| Outcome: | The proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models on the dataset. |
CancerEmo: A Dataset for Fine-Grained Emotion Detection (2020.emnlp-main)
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| Challenge: | a lack of large annotated datasets hinders emotion detection in the health domain . a recent study shows that online sharing of emotions is beneficial to a patient's progress . |
| Approach: | They propose an emotion dataset annotated with eight fine-grained emotions from an online health community. |
| Outcome: | The proposed model achieves an average F1 of 71% on the cancerEmo dataset . the best model achieve a higher F1 than the previous model, which was improved using domain-specific pre-training. |
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022.emnlp-main)
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| Challenge: | Large-scale crises such as the COVID-19 pandemic cause emotional turmoil worldwide. |
| Approach: | They propose a method to jointly detect emotions and summarize emotion triggers in social media posts related to COVID-19. |
| Outcome: | The proposed method can detect emotions and summarize emotions in long social media posts. |