Image

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.

Request demo

Image
Technology developed and deployed with the world’s leading organizations
Image

Overview

Decode Sentiments in Shades of Gray

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

Image

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.

Image

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.

Image

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.

Image

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

Image

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

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

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

Result

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

6 MONTHS
of crowd-worker labels replaced

+18.5

precision percentage points

+28.5

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.

Research

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

Image

Data Scientist Friendly

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

Domain Expert Friendly

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

Developer Friendly

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

Snorkel Solutions

Find more data-first solutions for other AI problems

Image
Filter Solutions
  • Document Application
  • Named Entity Recognition
  • Information Extraction
  • Sentiment Analysis
  • Anomaly Detection
  • Finance
  • Healthcare
  • Insurance
  • Use Cases