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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.

Use Cases

Sentiment Analysis Customized for Your Workflow

FINANCIAL SERVICES

Interaction Analytics
Banks can analyze live interactions and surveys to identify customer sentiment and remove points of friction.

SOFTWARE

Customer Support
By understanding customer sentiment, software companies can invest in tech support to improve customer engagement.

RETAIL

Brand Monitoring
Retailers can analyze social media posts or consumer surveys to assess customer response to branding changes.

HEALTHCARE

Mental Health Support
Clinicians can screen patient messages for indications of possible depression, suicidal thoughts, or dementia.

TELECOM & CYBER

Customer Service
By understanding customer sentiment, telecom organizations can invest in IT support to alleviate pain points.

FINANCIAL SERVICES

News Analytics
Hedge funds can improve their trading strategy by analyzing sentiments towards stocks in financial news.

Case Study

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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.

RESEARCH

Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision…

M. Chen, et al, 2020

RESEARCH

The Role of Massively Multi-Task and Weak Supervision in Software 2.0

A. Ratner, et al, CIDR 2019

RESEARCH

Software 2.0 and Snorkel: Going Beyond Hand-Labeled Data

C. Ré, KDD 2018 (invited)

RESEARCH

Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data

P. Varma, et al, 2017

RESEARCH

Snuba: Automating Weak Supervision to Label Training Data

P. Varma and C. Ré, VLDB 2019

RESEARCH

Snorkel: Rapid Training Data Creation With Weak Supervision

A. Ratner, et al. VLDB 2018

BLOG

Snorkel AI:
Putting Data First in ML Development

Alex Ratner

RESEARCH

Osprey: Weak Supervision of Imbalanced Extraction Problems without Code

E. Bringer, et al. DEEM @ SIGMOD 2019

RESEARCH

Interactive Programmatic Labeling for Weak Supervision

B. Cohen-Wang, et al, KDD 2019

RESEARCH

Fast & Three-rious: Speed Up Weak Supervision with Triplet Methods

D. Fu, et al, ICML 2020

BLOG

Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision

Abraham Starosta

RESEARCH

Bootstrapping Conversational Agents with Weak Supervision

N. Mallinar, et al. AAAI 2019

An End-to-end ML Platform

Designed for Collaboration

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Data Scientist Friendly

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

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

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