Sentiment analysis

Build AI-powered sentiment analysis applications to detect sentiments at the level of words, sentences, paragraphs, or documents, in a fraction of the time using programmatic labeling with Snorkel Flow.

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Data-centric AI technology developed at the Stanford AI Lab and proven at world-leading companies.

How Snorkel Flow works

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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Higher-accuracy models

Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
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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.
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Better SME collaboration

Build complex classification apps intuitively while preserving natural information about data taxonomies with subject matter expert (SME) collaboration.

Sentiment analysis

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.

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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
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Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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March 21, 2022
Snorkel AI welcomes industry leaders to the team

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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills

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February 24, 2021
The Data-First Enterprise AI Revolution

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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning

Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI

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Date: Jan 25, 2022
The Principles of Data-Centric AI Development

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Date: Jan 5, 2022
Meet the Snorkelers

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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications

Research
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2022
Universalizing Weak Supervision

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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records

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2017
Rapid Training Data Creation with Weak Supervision

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2016
Data Programming: Creating Large Datasets Quickly

Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials

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February 18, 2022
Google used Snorkel to build and adapt content classification models

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2019
Intel used Snorkel to accelerate sales and marketing agents

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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages

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The Future of

Data-Centric AI


August 3-4, 2022 | Virtual

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