1,003 People - Emotional Video Data

Emotional video

multiple races

multiple indoor scenes

multiple age groups

multiple languages

multiple emotions

11 types of facial emotions

15 types of inner emotions

feeling

passion

sentiment

excitement

sensation

affection

intensity

ardor

sensibility

fervor

vehemence

love

response

warmth

zeal

emotional

pathos

agitation

spirit

affectivity

enthusiasm

fervency

perturbation

impression

sympathy

sadness

sentimentality

thrill

eagerness

poignancy

commotion

emotionalism

fire

soul

animation

emotionality

responsiveness

sensitiveness

disturbance

energy

expression

tenderness

pitifulness

plaintiveness

poignance

sentiments

vibes

anger

empathy

feelings

Emotional Video Data,including multiple races, multiple indoor scenes, multiple age groups, multiple languages, multiple emotions (11 types of facial emotions, 15 types of inner emotions). For each sentence in each video, annotated emotion types (including facial emotions and inner emotions), start & end timestamp, text transcription.This dataset can be used for tasks such as emotion recognition and sentiment analysis, enhancing model performance in real and complex tasks.rnQuality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

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Specifications

Format

The video data format is .mp4, the annotation file format is .json;

Content category

Including multiple races, multiple indoor scenes, multiple age groups, multiple languages, multiple emotions (11 types of facial emotions, 15 types of inner emotions);

Recording condition

Indoor;

Recording device

Camera or smartphone;

Contributor

1,003 people, each person has one or several videos with multiple emotions; Race distribution: 232 people of Asian, 614 people of Caucasian, 157 people of black race; gender distribution: 410 people of male, 593 people of female; age distribution: 569 young people, 330 middle-aged people and 104 old-aged people;

Features of annotation

For each sentence in each video, annotated emotion types (including facial emotions and inner emotions), start & end timestamp, text transcription;

Accuracy Rate

Collecting accuracy: according to the 'collecting requirement', the collecting accuracy is over 97%; Label annotation accuracy: the accuracy of language, race, gender and age group labeling is over 97%; file annotation accuracy: the word accuracy rate of text transcription is over 85%;

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