Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for emotion classification are expensive and require a large corpus of data. |
| Approach: | They propose a method for creating a semi-automatically constructed emotion corpus by correcting errors in the corpus. |
| Outcome: | The proposed method improves the quality of the emotion labels by correcting errors. |
Similar Papers
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
An Analysis of Annotated Corpora for Emotion Classification in Text (C18-1)
Copied to clipboard
| Challenge: | Several datasets have been annotated and published for classification of emotions. |
| Approach: | They aggregated emotion corpora in a common file format with a shared annotation schema . they perform cross-corpus classification experiments to gain insight and a better understanding of differences . |
| Outcome: | The proposed model can be trained on a subset of corpora, but not on all corporata. |
Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning (C18-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model improves the results across different genres compared to a single model trained on a genre. |
Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text. |
| Approach: | They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. |
| Outcome: | The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset. |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
Copied to clipboard
| 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 . |
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus (L18-1)
Copied to clipboard
| Challenge: | Existing methods for predicting emotion categories are limited due to their multi-label nature . e.g. anger, joy, sadness are difficult to predict due to inherent multi-genre nature - a problem that is often overlooked in single-genrete text. |
| Approach: | They propose to expand existing annotated data to include 8 emotions from Plutchik's Wheel of Emotions . they explore the effectiveness of clause annotation in sentence-level emotion detection and classification . |
| Outcome: | The proposed system is the first to target the clause level and provides emotion classification for movie reviews datasets. |
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level (C18-1)
Copied to clipboard
| Challenge: | Emotion Representation Mapping (ERM) is an alternative to Word Emotion Induction (WEI) for automatic emotion lexicon construction. |
| Approach: | They propose a neural network approach to ERM that converts existing emotion ratings from one representation format into another by mapping Valence-Arousal-Dominance annotations into Ekman’s Basic Emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art in 13 languages and is almost as reliable as human annotations even in cross-lingual settings. |
An Event-comment Social Media Corpus for Implicit Emotion Analysis (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for identifying implicit emotions have been poor in analyzing explicit emotions. |
| Approach: | They propose to construct a Chinese eventcomment social media emotion corpus which deals with both explicit and implicit emotions with more emphasis being placed on the implicit ones. |
| Outcome: | The proposed corpus will be useful for both explicit and implicit emotion classification and detection as well as event classification. |
Towards Label-Agnostic Emotion Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |
CARER: Contextualized Affect Representations for Emotion Recognition (D18-1)
Copied to clipboard
| Challenge: | Existing methods to model emotion-relevant content are based on rule-based and statistics-based approaches. |
| Approach: | They propose a semi-supervised graph-based algorithm to produce rich structural descriptors . they use word embeddings to evaluate the algorithm on emotion recognition tasks . |
| Outcome: | The proposed method outperforms state-of-the-art methods on emotion recognition tasks. |