| Challenge: | Social networks have created datasets of opinions of users that focus on the writers' perspective, which does not consider the source that provokes those opinions. |
| Approach: | They propose to analyze opinions of Twitter users' after reading a news article and use it to predict the distribution of emotions. |
| Outcome: | The proposed dataset aims to explore how the six emotions are expressed by Twitter users' after reading a news article. |
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Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
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| 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. |
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 Individualized News Affective Response Dataset (2024.acl-srw)
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| Challenge: | a new dataset captures subjective affective responses to news headlines . current methods to assess emotion detection ignore subjective differences in groups and individuals . |
| Approach: | They propose a large-scale dataset capturing subjective affective responses to news headlines . the dataset includes Facebook post screenshots from popular UK media outlets . |
| Outcome: | The proposed dataset captures subjective affective responses to headlines from popular media outlets. |
Prediction of People’s Emotional Response towards Multi-modal News (2022.aacl-main)
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Ge Gao, Sejin Paik, Carley Reardon, Yanling Zhao, Lei Guo, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
| Challenge: | BU-NEmo dataset extends from 320 to 1,297 news headline and lead image pairings and collects 38,910 annotations in a crowdsourcing experiment. |
| Approach: | They extend the U.S. gun violence news-to-emotions dataset from 320 to 1,297 news headline and lead image pairings and collect annotations in a crowdsourcing experiment. |
| Outcome: | The proposed models outperform baseline models on the NEmo+ dataset by large margins across several metrics. |
Author’s Sentiment Prediction (2020.coling-main)
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| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
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. |
Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)
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| Challenge: | a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task . |
| Approach: | They propose a task to estimate the response a persona might have upon seeing a news message. |
| Outcome: | The proposed task estimates the response a persona might have upon seeing a news message. |
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 . |
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. |
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)
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| Challenge: | Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. |
| Approach: | They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline. |
| Outcome: | The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies. |