Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Unsupervised Extractive Summarization of Emotion Triggers (2023.acl-long)
Copied to clipboard
| 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. |
Emotion analysis and detection during COVID-19 (2022.lrec-1)
Copied to clipboard
| 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 . |
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
Copied to clipboard
| Challenge: | A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. |
| Approach: | They use a large-scale dataset from Chinese microblog Sina Weibo to examine readers' responses to online discussion topics. |
| Outcome: | The proposed model outperforms the human model in predicting social emotions in a multilabel classification setting. |
EMO-KNOW: A Large Scale Dataset on Emotion-Cause (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing datasets focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. |
| Approach: | They propose to use 9.8 million cleaned tweets to create a large-scale dataset of emotion causes, derived from 9.8 millions tweets over 15 years. |
| Outcome: | The proposed dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. |
Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing work has sought to identify what triggers or causes a particular emotion, but the relationship between those triggers and the prediction of emotion detection models is little understood. |
| Approach: | They propose a dataset to evaluate the ability of large language models to identify emotion triggers . they compare features considered important for emotion prediction models to those considered less salient . |
| Outcome: | The proposed dataset compares large language models and fine-tuned models on social media posts . it shows that emotion triggers are not considered salient features for emotion prediction models . |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
Copied to clipboard
Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text (2022.lrec-1)
Copied to clipboard
| Challenge: | Social distancing orders are the most effective strategy to reduce the spread of COVID-19 in 2020 . |
| Approach: | They propose to use NLP methods in a causal mediation scenario to emphasize the use of NLP and economics to decouple the effect of government restrictions on mobility from the effect that occurs due to public perception of the COVID-19 strategy. |
| Outcome: | The proposed model decouples the effect of government restrictions on mobility behavior from the effect that occurs due to public perception of the COVID-19 strategy in a country. |
Stance Detection in COVID-19 Tweets (2021.acl-long)
Copied to clipboard
| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Fine-Grained Emotion Detection in Health-Related Online Posts (D18-1)
Copied to clipboard
| Challenge: | Emotion detection from health-related posts is based on a health-specific vocabulary that people use in OHCs. |
| Approach: | They propose to use deep neural networks and lexicon-based features to detect emotions in health-related posts. |
| Outcome: | The proposed method uses high-level and abstract features derived from deep neural networks combined with lexicon-based features to detect emotions. |
Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce (2025.findings-emnlp)
Copied to clipboard
Arnav Attri, Anuj Attri, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing research has not explored the joint task of emotion detection and explanatory span identification in e-commerce reviews. |
| Approach: | They propose a joint task unifying Emotion detection and Opinion Trigger extraction (EOT) which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories). |
| Outcome: | The proposed framework surpasses zero-shot and chain-of-thought techniques across e-commerce domains. |