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


Decode Sentiments in Shades of Gray

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


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.


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.


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.


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.

Use Cases

Sentiment Analysis Customized for Your Workflow

Case Study


Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.

Rapidly changing sales goals make social media monitoring difficult to maintain.

Deployed Snorkel to replace months-long crowd-worker effort with cheap and fast template-based programmatic labeling.


Better performance and major cost savings for sales & marketing and customer analytics.

of crowd-worker labels replaced


precision percentage points


coverage percentage points

How Snorkel Flow Works

Build Nuanced AI Apps For Sentiment Analysis

Build sentiment analysis applications that classify the polarity of text. Programmatically label text data with no-code-required labeling functions, then train models to identify nuanced emotions and opinions in any textual format. Fine-tune your classification schema, labeling functions, and models until your sentiment analysis application is production-ready.


Based on Years of Novel Research
Learn more about groundbreaking techniques for machine learning and weak supervision developed by the Snorkel AI team at Stanford AI Lab and beyond.

An End-to-end ML Platform

Designed for Collaboration


Data Scientist Friendly

  • Integrated Jupyter notebooks
  • Instant analysis tools
  • Ready-to-use models

Domain Expert Friendly

  • Intuitive, no-code UI
  • Rich dashboards and visualizations
  • Full-featured, push-button error analysis

Developer Friendly

  • Platform access via Python SDK
  • Online or batch API deployment
  • Containerized software for cloud or on-premises deployments

Snorkel Solutions

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