Sentiment Analysis




Build AI-powered sentiment analysis applications to detect sentiments, at the level of words, sentences, paragraphs, or documents, in a fraction of time without hand-labeling training data using Snorkel Flow.

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Technology developed and deployed with the world’s leading organizations
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Overview —

Decode Sentiments in Shades of Gray


Rapidly and precisely build ML models to quantify and analyze complex sentiments in virtually any text.



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Faster, Lower-cost Development
Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.
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Rapidly Adaptable
Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.
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Easier SME Collaboration
Enable Subject Matter Experts (SME) to define polarity, subjectivity or tone, and refine schematic boundaries programmatically using a no-code or Jupyter notebook-based interface.
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Flexible Integrations
Easily integrate labeling, training, and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.






Industry Use Cases —

Sentiment Analysis Customized for Your Workflow


Build industry-specific AI applications combining state-of-the-art machine learning approaches with industry-specific best practices and last-mile connectors, all on an enterprise-scale platform.



FINANCIAL SERVICES



Contract Intelligence

Banks can classify contracts by terms and conditions to smoothly ensure regulatory complience.
TELECOM & CYBER



Customer Segmentation

Telecom organizations can classify customer usage documents to target promotional offers.
HEALTHCARE



Clinical Trial Matching

Biotech organizations can classify patient records to identify actionable clinical trial candidates.
INSURANCE



Risk Classification

Insurance underwriters can classify policy documents by behavioral or occupational variables to assess risk.
SOFTWARE



Search Engine Optimization

Software companies can recognize named entities in customer search queries and to optimize website content.
RETAIL



Product Recommendation

E-commerce sites can recognize entities in product descriptions (price, key words, etc.) to improve recommender systems.






An End-to-end ML Platform —

Designed for Collaboration




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For Data Scientists


  • Ready-to-use model zoo
  • Auto-generated analysis tools
  • Integrated Python notebooks
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For Domain Experts


  • Rich data annotation suite
  • Intuitive, no-code labeling UI
  • Model error analysis reports
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For Developers


  • Fully interoperable API and web UI
  • Write custom operators with Python SDK
  • Integrations to deploy models at scale






Resources —

Explore More About Snorkel


Learn more about groundbreaking techniques for programmatic labeling and weak supervision developed by Team Snorkel and the broader data science community.