| Challenge: | Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently. |
| Approach: | They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions. |
| Outcome: | The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts. |
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How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs (C18-1)
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| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
| Approach: | They propose to combine convolutional and fully connected layers in a non-sequential manner to train deep multi-task learning models trained for all emotions at once in unified architecture. |
| Outcome: | The proposed model outperforms the previous system by 0.044 or 4.4% on the WASSA’17 EmoInt shared task dataset. |
Fine-Grained Emotion Detection in Health-Related Online Posts (D18-1)
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| 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. |
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts (D18-1)
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| Challenge: | Existing work on explaining classifier decisions has not addressed local feature redundancy . a common way to explain why a model classified an example is to extract a sparse subset of features that were responsible for the decision . |
| Approach: | They propose an adversarial method for producing high-recall explanations of text classifier decisions . they use a method which scans the residual of attention for remaining predictive signal . |
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Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)
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| Challenge: | Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. |
| Approach: | They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation. |
| Outcome: | The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)
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| Challenge: | Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits. |
| Approach: | They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text. |
| Outcome: | The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets. |
Detecting Gang-Involved Escalation on Social Media Using Context (D18-1)
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Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
| Challenge: | In cities such as Chicago, gang-involved youth have increasingly turned to social media to post about their experiences and intents online. |
| Approach: | They propose a system that uses domain-specific resources and contextual representations of the emotional and semantic content of the user’s recent tweets and their interactions with other users to detect Aggression and Loss in social media posts. |
| Outcome: | The proposed system improves on a large unlabeled dataset and incorporates contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. |
Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
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| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
| Outcome: | The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs. |
Personal Bias in Prediction of Emotions Elicited by Textual Opinions (2021.acl-srw)
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| Challenge: | Various models for emotion recognition have been used in different studies. |
| Approach: | They propose to use an annotated corpus to estimate personal emotional bias to estimate individual responses to texts . they propose to employ a new BERT-based transformer architecture to predict emotions from an individual human perspective. |
| Outcome: | The proposed method improves the quality of personalized reasoning and may boost the quality and reliability of content recommendation systems. |