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.
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.
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.
Banks can analyze live interactions and surveys to identify customer sentiment and remove points of friction.
By understanding customer sentiment, software companies can invest in tech support to improve customer engagement.
Retailers can analyze social media posts or consumer surveys to assess customer response to branding changes.
Mental Health Support
Clinicians can screen patient messages for indications of possible depression, suicidal thoughts, or dementia.
TELECOM & CYBER
By understanding customer sentiment, telecom organizations can invest in IT support to alleviate pain points.
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.
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 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.
Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision…M. Chen, et al, 2020
The Role of Massively Multi-Task and Weak Supervision in Software 2.0A. Ratner, et al, CIDR 2019
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training DataP. Varma, et al, 2017
Osprey: Weak Supervision of Imbalanced Extraction Problems without CodeE. Bringer, et al. DEEM @ SIGMOD 2019
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
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments
Find more data-first solutions for other AI problems
- Document Application
- Named Entity Recognition
- Information Extraction
- Sentiment Analysis
- Anomaly Detection
- Use Cases