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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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SpecificationsSpecifications
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 accuracy of text transcription is over 85%;
Sample Sample
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