GoEmotions: A Dataset of Fine-Grained Emotions

• URL: http://arxiv.org/abs/2005.00547v2
• Date: Wed, 3 Jun 2020 00:31:11 GMT
• Title: GoEmotions: A Dataset of Fine-Grained Emotions
• Authors: Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade and Sujith Ravi
• Abstract summary: We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. Our BERT-based model achieves an average F1-score of.46 across our proposed taxonomy, leaving much room for improvement.
• Score: 16.05879383442812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.

Related papers

• Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning [55.127202990679976]
  We introduce the MERR dataset, containing 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories. This dataset enables models to learn from varied scenarios and generalize to real-world applications. We propose Emotion-LLaMA, a model that seamlessly integrates audio, visual, and textual inputs through emotion-specific encoders.
  arXiv  Detail & Related papers  (2024-06-17T03:01:22Z)
• Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning [1.124958340749622]
  The primary goal of this paper is to address the challenges of detecting subtle emotions in text. The findings offer valuable insights into addressing the challenges of emotion detection in text.
  arXiv  Detail & Related papers  (2024-03-10T06:30:54Z)
• eMotions: A Large-Scale Dataset for Emotion Recognition in Short Videos [7.011656298079659]
  The prevailing use of short videos (SVs) leads to the necessity of emotion recognition in SVs. Considering the lack of SVs emotion data, we introduce a large-scale dataset named eMotions, comprising 27,996 videos. We present an end-to-end baseline method AV-CPNet that employs the video transformer to better learn semantically relevant representations.
  arXiv  Detail & Related papers  (2023-11-29T03:24:30Z)
• Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speech [3.86122440373248]
  We propose a soft labeling system to capture gradational emotional intensities. Using the Whisper encoder and data augmentation methods inspired by contrastive learning, our method emphasizes the temporal dynamics of emotions. We publish our open source model weights and initial promising results after fine-tuning on Hume-Prosody.
  arXiv  Detail & Related papers  (2023-11-15T00:09:21Z)
• Automatic Emotion Modelling in Written Stories [4.484753247472559]
  We propose a set of novel Transformer-based methods for predicting emotional signals over the course of written stories. We explore several strategies for fine-tuning a pretrained ELECTRA model and study the benefits of considering a sentence's context. Our code and additional annotations are made available at https://github.com/lc0197/emotion_modelling_stories.
  arXiv  Detail & Related papers  (2022-12-21T21:46:01Z)
• MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild [56.61912265155151]
  We propose MAFW, a large-scale compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment.
  arXiv  Detail & Related papers  (2022-08-01T13:34:33Z)
• DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning [0.0]
  We propose DeepEmotex an effective sequential transfer learning method to detect emotion in text. We conduct an experimental study using both curated Twitter data sets and benchmark data sets. DeepEmotex models achieve over 91% accuracy for multi-class emotion classification on test dataset.
  arXiv  Detail & Related papers  (2022-06-12T03:23:40Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation [85.20533077846606]
  Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain. In this paper, we focus on UDA in visual emotion analysis for both emotion distribution learning and dominant emotion classification. We propose a novel end-to-end cycle-consistent adversarial model, termed CycleEmotionGAN++.
  arXiv  Detail & Related papers  (2020-11-25T01:31:01Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)
• EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle [71.47160118286226]
  We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods.
  arXiv  Detail & Related papers  (2020-03-14T19:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.