README.md

---
license: mit
task: text-classification
language: ko
tags:
  - AI
  - VPA
---

# View Pulse AI (VPA) Multimodal Media Behavior Dataset

> **Patent Pending:** Protected by national patents (Application Nos: 10-2026-0047313, 10-2026-0047312). All rights reserved by ViewPulse AI.

## Overview
View Pulse AI (VPA) is a proprietary multimodal dataset designed for predictive audience behavior analysis. This dataset bridges raw video content with real-time viewer engagement, optimized for AI-driven rating prediction engines.

## Technical Specifications
- **Format**: Parquet (AI-Ready)
- **Features**: 
    - `Audio_i`: Tension and atmosphere spectral features.
    - `NLP_Vector (L_i)`: Contextual sentiment analysis.
    - `Vision_i`: Scene composition and dynamics.
    - `Temporal_i`: Sequence-based temporal behavioral patterns.
- **Label**: Reaction_Class (-1, 0, 1) for audience behavior categorization.

## Key Value Proposition
Our SBV (Scene Behavior Vector) engine utilizes this dataset to predict viewer retention with high precision, enabling data-backed content production and strategic broadcasting.

## Licensing & Contact
Proprietary License. All rights reserved by ViewPulse AI. For commercial licensing, B2B collaboration, or full dataset (Engine API) access, please contact us directly:
* **Email:** yooncm21@gmail.com

## 🚀 Quick Start (Usage)
VPA 데이터셋은 AI 학습 및 데이터 분석에 즉시 투입할 수 있도록 고도로 정제된 **Parquet** 포맷으로 제공됩니다. 

가장 빠르게 1340D 다중 모달 데이터를 체험해보고 싶으시다면, 아래 버튼을 눌러 웹 환경에서 즉시 코드를 실행해 보세요!

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WrBB452eG8oL3EbVDc9j8rSqTx8z-dzK?usp=sharing)

### Method 1: Using Hugging Face `datasets` (For AI Engineers)
파이토치(PyTorch) 등 딥러닝 모델 학습에 바로 활용할 때 추천하는 방식입니다.

```python
# pip install datasets
from datasets import load_dataset
import pandas as pd

# Load the VPA Multimodal dataset
dataset = load_dataset("ViewPulseAI/viewpulse-vpa-sample")

# Convert to Pandas DataFrame for easy analysis
df = dataset['train'].to_pandas()
print(df[['Time_Code', 'Scene_Description', 'Audio_Label']].head())