Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation
This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, \textit{Sentimentator}, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisSimilar Papers 제목 키워드 기반
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection
We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing ne…
Sentiment AnalysisBeyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation
Emotional text-to-speech (E-TTS) is central to creating natural and trustworthy human-computer interaction. Existing systems typically rely on sentence-level control through predefined labels, reference audio, or natural…
Speech SynthesisEmoSpace: Fine-Grained Emotion Prototype Learning for Immersive Affective Content Generation
Emotion is important for creating compelling virtual reality (VR) content. Although some generative methods have been applied to lower the barrier to creating emotionally rich content, they fail to capture the nuanced em…
Image OutpaintingMETTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer
Previous multilingual text-to-speech (TTS) approaches have considered leveraging monolingual speaker data to enable cross-lingual speech synthesis. However, such data-efficient approaches have ignored synthesizing emotio…
DisentanglementDiversityQuantizationSpeech Synthesis+2EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning
This study introduces EM2LDL, a novel multilingual speech corpus designed to advance mixed emotion recognition through label distribution learning. Addressing the limitations of predominantly monolingual and single-label…
Self-Supervised LearningEmotion Recognition