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. |
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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. |
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 . |
Emotion Detection with Neural Personal Discrimination (D19-1)
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| 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. |
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Exploring Optimism and Pessimism in Twitter Using Deep Learning (D18-1)
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| Challenge: | Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need it. |
| Approach: | They propose deep learning models to predict optimism and pessimism in Twitter . they also show that a sentiment classifier would not be sufficient for predicting optimism and psi . |
| Outcome: | The proposed models outperform traditional machine learning classifiers on optimism and pessimism in Twitter. |
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. |
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. |
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. |
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Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning (C18-1)
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| Challenge: | Sentence-level emotion detection is a challenging task due to subjectivity of emotion. |
| Approach: | They propose a model to address genre robustness in a multi-task learning problem . they use a genre-based corpus to train a neural net model with different genres . |
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Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
| Approach: | They propose to use emotional patterns identified by clinical practitioners to enhance the prediction capabilities of a mental illness detection model built using a deep neural network architecture. |
| Outcome: | The proposed method achieves a task-specific AUC higher than 0.90 . it compares multi-task learning with multi-channel convolutional neural network and multiple inputs to methods such as multi-class classification . |
Affect inTweets: A Transfer Learning Approach (2020.lrec-1)
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| Challenge: | Existing machine learning models require considerable effort to design task specific features to understand affectual states of people. |
| Approach: | They propose a transfer-learning based approach to infer the affectual state of a person from tweets. |
| Outcome: | The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets. |