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Register a new userUncovering the Limits of Text-based Emotion Detection
يكشف عن حدود اكتشاف العاطفة القائمة على النص
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تحديد المشاعر من النص أمر حاسم لمجموعة متنوعة من مهام العالم الحقيقي.نحن نعتبر أكبر فورسورا المتوفر الآن لتصنيف العاطفة الآن: جيموتونات، مع رسائل 58 ألفا تسمى القراء، والتنفيس، مع رسائل 33 مترا مصممة الكاتب.نقوم بتصميم معيارا وتقييم العديد من المساحات الميزة وخوارزميات التعلم، بما في ذلك نموذجين بسيطين ولكن الرواية أعلى بيرت التي تتفوق على خطوط الأساس القوية السابقة على GAEMOTION.من خلال تجربة مع مشاركين بشريين، نحلل أيضا الاختلافات بين كيفية التعبير عن المشاعر وكيفية إدراك القراء لهم.تشير نتائجنا إلى أن العواطف التي أعرب عنها الكتاب أصعب تحديدها من العواطف التي ينظر إليها القراء.نحن نشارك واجهة الويب العامة للباحثين لاستكشاف نماذجنا.
Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages. We design a benchmark and evaluate several feature spaces and learning algorithms, including two simple yet novel models on top of BERT that outperform previous strong baselines on GoEmotions. Through an experiment with human participants, we also analyze the differences between how writers express emotions and how readers perceive them. Our results suggest that emotions expressed by writers are harder to identify than emotions that readers perceive. We share a public web interface for researchers to explore our models.
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We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover's Distance) loss
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