Amazon Web Services Case Studies Zignal Labs Performs Next-Level Sentiment Analysis Using Amazon SageMaker and Amazon EC2

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	Zignal Labs Performs Next-Level Sentiment Analysis Using Amazon SageMaker and Amazon EC2 Amazon Web Services

Zignal Labs Performs Next-Level Sentiment Analysis Using Amazon SageMaker and Amazon EC2

Amazon Web Services

Technology Category	Analytics & Modeling - Machine Learning Platform as a Service (PaaS) - Data Management Platforms
Applicable Functions	Sales & Marketing Business Operation
Use Cases	Predictive Quality Analytics Real-Time Location System (RTLS)
Services	Data Science Services Cloud Planning, Design & Implementation Services
Challenge	Zignal Labs, a company that helps its customers measure brand impact, mitigate reputation risks, and inform data-driven communications strategies, wanted to take existing sentiment classification techniques to the next level with a focus on reputation polarity. The company wanted to offer a solution that identifies the actual positive or negative impact of online content on a brand. Zignal Labs was all too familiar with the limitations of third-party sentiment-analysis solutions, having experimented with many of them itself. Some of these tools presented problems around scalability, and some weren't well suited to all the different media sources Zignal needed to track. Read More
About Customer	Zignal Labs is a company that offers solutions that analyze the entire digital media landscape to deliver instant insights for the company’s Fortune 1000 customers. The company is based in San Francisco, California, and employs 100 people. Zignal Labs helps its customers measure brand impact, mitigate reputation risks, and inform data-driven communications strategies. The company has been using Amazon Web Services (AWS) since its founding in 2011. Read More
Solution	Zignal Labs used AWS to build a sentiment-analysis pipeline that could better understand the nuances of brand mentions across the entire digital landscape. The Zignal Labs pipeline does this with a machine-learning solution based on Amazon SageMaker, a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine- learning models at any scale. It also uses Amazon EC2 C5 instances, featuring Intel Xeon Scalable (Skylake) processors. In addition to Amazon SageMaker and Amazon EC2 C5 instances, Zignal Labs utilizes a distributed streaming architecture, including Spark, Storm, and Elasticsearch, to ingest more than three billion documents per month. The collected articles, tweets, blog posts, reddit posts, broadcast television programs, and comment threads are analyzed in Amazon SageMaker using machine-learning models that are retrained daily with inputs that include label data from “Human Intelligence Tasks” performed by workers from the Amazon Mechanical Turk (Amazon MTurk) marketplace. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Platform as a Service (PaaS) - Data Management Platforms

Applicable Functions

Sales & Marketing

Business Operation

Use Cases

Predictive Quality Analytics

Real-Time Location System (RTLS)

Services

Data Science Services

Cloud Planning, Design & Implementation Services

Challenge

Zignal Labs, a company that helps its customers measure brand impact, mitigate reputation risks, and inform data-driven communications strategies, wanted to take existing sentiment classification techniques to the next level with a focus on reputation polarity. The company wanted to offer a solution that identifies the actual positive or negative impact of online content on a brand. Zignal Labs was all too familiar with the limitations of third-party sentiment-analysis solutions, having experimented with many of them itself. Some of these tools presented problems around scalability, and some weren't well suited to all the different media sources Zignal needed to track.

About Customer

Zignal Labs is a company that offers solutions that analyze the entire digital media landscape to deliver instant insights for the company’s Fortune 1000 customers. The company is based in San Francisco, California, and employs 100 people. Zignal Labs helps its customers measure brand impact, mitigate reputation risks, and inform data-driven communications strategies. The company has been using Amazon Web Services (AWS) since its founding in 2011.

Solution

Zignal Labs used AWS to build a sentiment-analysis pipeline that could better understand the nuances of brand mentions across the entire digital landscape. The Zignal Labs pipeline does this with a machine-learning solution based on Amazon SageMaker, a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine- learning models at any scale. It also uses Amazon EC2 C5 instances, featuring Intel Xeon Scalable (Skylake) processors. In addition to Amazon SageMaker and Amazon EC2 C5 instances, Zignal Labs utilizes a distributed streaming architecture, including Spark, Storm, and Elasticsearch, to ingest more than three billion documents per month. The collected articles, tweets, blog posts, reddit posts, broadcast television programs, and comment threads are analyzed in Amazon SageMaker using machine-learning models that are retrained daily with inputs that include label data from “Human Intelligence Tasks” performed by workers from the Amazon Mechanical Turk (Amazon MTurk) marketplace.

Impact #1	The new Zignal Labs sentiment pipeline is delivering results that show at least 30 percent improvement in precision compared to prior methods, helping the company win and retain customers.
Impact #2	The solution was built on AWS, increased accuracy is being delivered at much lower cost than would be possible using third-party sentiment-analysis solutions.
Impact #3	Building the new sentiment pipeline on AWS reduced the cost of both its initial development and ongoing operations by 90 percent.

Impact #1

The new Zignal Labs sentiment pipeline is delivering results that show at least 30 percent improvement in precision compared to prior methods, helping the company win and retain customers.

Impact #2

The solution was built on AWS, increased accuracy is being delivered at much lower cost than would be possible using third-party sentiment-analysis solutions.

Impact #3

Building the new sentiment pipeline on AWS reduced the cost of both its initial development and ongoing operations by 90 percent.

Benefit #1	Improved precision of sentiment analysis by 30%
Benefit #2	Reduced development and operations costs by 90%

Benefit #1

Improved precision of sentiment analysis by 30%

Benefit #2

Reduced development and operations costs by 90%

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Overview

Zignal Labs Performs Next-Level Sentiment Analysis Using Amazon SageMaker and Amazon EC2

Operational Impact

Quantitative Benefit