Movie Feelings provides three independent emotion feature sets for 1,500 films. The features were generated using NLP, transformers, and GPT-4o. The dataset's author, organization, and last update date are unknown.
Use Cases
- Train sentiment classification models based on emotion features.
- Analyze emotional trends across films based on NLP-derived features.
- Benchmark transformer models for affective computing tasks.
- Compare emotion feature extraction methods (NLP, transformers, GPT-4o).
Strengths
- Includes 1,500 films.
- Provides three independent emotion feature sets.
- Features derived from multiple methods: NLP, transformers, and GPT-4o.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Collection Method
- Features generated using NLP, transformers, and GPT-4o.