News analytics

Build custom, AI-powered news analytics applications that extract entities, events, and relationships from diverse news sources using Snorkel Flow.

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Deeply understand the impact of news

Snorkel Flow lets you build custom applications that analyze news and extract entities, events and relationships precisely. Go beyond brittle, off-the-shelf data feeds today–without the bottleneck of manual labeling.

High-accuracy models

Develop highly accurate models to decrease the potential of errors with guided iteration and built-in analysis tools.
With rising demand for affluent stay-at-home consumers, interactive fitness bike maker Peloton [ENTITY] agreed to buy [EVENT] Precor [ENTITY], a major provider of workout machines to gyms and hotels, for $420M [PRICE], its biggest purchase to date.

Faster, lower-cost extraction

Use programmatic labeling to train models that extract custom attributes in minutes instead of spending weeks or months on expensive hand-labeling.
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Rapidly adaptable

Rapidly build apps that adapt to new attributes in a fraction of the time. Retrain models with ease to changing input data or business objectives.
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Sentiment
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Intent
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Novelty
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Topic
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Temporal

Flexible integrations

Avoid vendor lock-in with easy integration of labeling, training, and analysis pipelines with diverse news sources or downstream applications using APIs or a Python SDK.
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News
outlets
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Social
media
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Financial
news

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Digital
feeds

Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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Private

Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.


Case study

Fortune 50 bank

In just weeks, a Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow.
Read more



Problem

The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.

Solution

The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.

Results

With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.

45x

faster compared to hand-labeling

+90

F1 score for news analytics application

+25%

performance gain over black box vendor system



Case Study

Fortune 50 Bank

A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read more



Problem

The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.

45x

faster compared to hand-labeling

Solution

The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.

+90

F1 score for news analytics application

Results

With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.

+25%

point performance gain over black box vendor system


Case Study

Fortune 50 Bank

A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read More



Problem

The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.

Solution

The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.

Results

With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.

45x

faster compared to hand-labeling

+90

F1 score for news analytics application

+25%

point performance gain over black box vendor system

A radically new approach to AI

Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.

With Snorkel Flow

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.
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Let's connect

Speed time to value, reduce costs, and unlock more AI possibility with the Snorkel Flow platform.
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