| --- |
| 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! |
| |
| [](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()) |
