Datadome LLC

The Sentiment Analysis Dataset is a high-quality corpus of realistic, labeled text samples designed to support training and evaluation of sentiment classification models. The dataset consists of natural-language sentences that reflect real-world customer feedback and user opinions, each annotated with a discrete sentiment label (positive, neutral, negative) and a continuous sentiment score to capture emotional intensity. All records are synthetically generated to resemble authentic user-generated text while remaining privacy-safe and suitable for commercial use.

The dataset is delivered as native Snowflake tables, enabling immediate integration into analytics, machine learning, and NLP workflows. Scope Spans multiple common feedback sources—including product reviews, social interactions, customer support conversations, and product feedback—to reflect how sentiment is expressed across different communication channels and contexts. Scale Tens of thousands of labeled text examples, with balanced sentiment distribution, provide sufficient coverage for model training, experimentation, benchmarking, and proof-of-concept development.

Additional volumes are available for advanced use cases via a private offer. Value High-quality sentiment labels with both categorical polarity and numeric sentiment scores for classification and regression tasks. Realistic language patterns that closely resemble real customer feedback while remaining fully synthetic and privacy-safe.

Built-in metadata (source domain) to enable segmentation, filtering, and domain-specific analysis. Zero-friction integration via Snowflake tables—query, join, and train models directly without additional preprocessing. Use this dataset to accelerate the development of sentiment-aware NLP systems—whether you’re fine-tuning transformer models, building customer feedback analytics, developing conversational AI, or validating sentiment pipelines in a secure, production-friendly environment.