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相关概念视频

Labeling Emotion01:20

Labeling Emotion

186
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Emotional Expression01:26

Emotional Expression

292
Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
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Physiology of Emotion01:20

Physiology of Emotion

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The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
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Empathy02:34

Empathy

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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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使用转移学习方法检测文本情绪.

Mahsa Hadikhah Mozhdehi1, AmirMasoud Eftekhari Moghadam1

  • 1Faculty of Computer and Information Technology, Islamic Azad University, Qazvin, Iran.

The Journal of supercomputing
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

使用EmotionalBERT进行转移学习可以提高文字情感检测,优于LSTM和GRU等传统模型. 这种方法需要更少的数据和训练时间来准确识别情绪.

关键词:
情绪的分类 情绪的分类情绪检测 情绪检测 情绪检测大型语言模型.自然语言处理自然语言处理.文本挖掘 (Text Mining) 是一种文字挖掘方式.转移学习转移学习

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 传统的深度学习模型 (LSTM,GRU,BiLSTM) 在自动化文本情感检测方面面临挑战.
  • 这些模型需要大量的数据集,大量的计算资源和长时间的培训时间.
  • 它们在较小的数据集中表现出局限性,容易发生灾难性遗忘.

研究的目的:

  • 证明转移学习技术的有效性,以增强文本情感检测.
  • 展示预训练模型在减少数据和培训时间的情况下捕捉上下文含义的能力.
  • 将转移学习模型的性能与传统的基于循环神经网络 (RNN) 的模型进行比较.

主要方法:

  • 使用了EmotionalBERT,这是一个基于变压器 (BERT) 双向编码器表示的预训练模型.
  • 在两个基准数据集上进行实验,以评估模型性能.
  • 专注于分析不同培训数据量对模型有效性的影响.

主要成果:

  • 与基于RNN的模型相比,使用EmotionalBERT的转移学习在文本情感检测方面取得了卓越的表现.
  • 该研究强调了EmotionalBERT的有效性,即使训练数据有限.
  • 当使用转移学习方法时,观察到培训时间减少.

结论:

  • 转移学习,特别是使用EmotionalBERT,为自动化文本情感检测提供了更高效和有效的解决方案.
  • 这种方法减轻了对大型数据集和广泛计算资源的需求.
  • 情绪BERT表现出强的性能,特别是在数据可用性有限的场景中.