Zignal Labs Performs Next-Level Sentiment Analysis Using Amazon SageMaker and Amazon EC2
---nasdaq--amzn_14.jpg "Amazon Web Services Logo")
Customer Company Size
SME
Region
- America
Country
- United States
Product
- Amazon SageMaker
- Amazon EC2 C5 Instances
- Zignal Enterprise media intelligence platform
Tech Stack
- Spark
- Storm
- Elasticsearch
- Amazon Mechanical Turk
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Cost Savings
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Leading Tools Manufacturer Transforms Operations with IoT
Stanley Black & Decker required transparency of real-time overall equipment effectiveness and line productivity to reduce production line change over time.The goal was to to improve production to schedule, reduce actual labor costs and understanding the effects of shift changes and resource shifts from line to line.
Case Study
IoT Data Analytics Case Study - Packaging Films Manufacturer
The company manufactures packaging films on made to order or configure to order basis. Every order has a different set of requirements from the product characteristics perspective and hence requires machine’s settings to be adjusted accordingly. If the film quality does not meet the required standards, the degraded quality impacts customer delivery causes customer dissatisfaction and results in lower margins. The biggest challenge was to identify the real root cause and devise a remedy for that.
Case Study
Jaguar Land Rover Speeds Order-to-Cash Cycle
At Jaguar Land Rover, vehicles physically move around the facility for testing, configuration setting, rework and rectification, leading to a longer search time to get each vehicle to its next process facility. The main goal is to minimize the vehicles' dwell time between end of line and the delivery chain which was previously a manually intensive process. Jaguar Land Rover's goal was to build on the success of an earlier RFID project and improve the efficiency of delivering vehicles to meet dealer orders.
Case Study
Improve Postal Mail and Package Delivery Company Efficiency and Service
Postal mail and package delivery company wanted to replace legacy yard management system, increase inbound and outbound yard velocity, improve priority parcel delivery time and accuracy, reduce workload and overtime, reduce driver detention and measure performance and utilization of yard resources.
Case Study
Hospital Management Solution
The Oncology Diagnosis and Treatment Center of Brasov wanted to give patients as much freedom to roam as possible, while at the same time ensuring optimal patient safety and security. The centre was in need of an adequate wireless voice communication and messaging solution that would give patients the confi dence that medical staff is always on call, and reduce stress levels of nurses and doctors when called in case of urgent need.
Case Study
Worker Tracking & Safety Monitoring in Construction
One of the main challenges facing the technology was to create a network within underground tunnels and another was to provide products that can withstand harsh working environment. The team used amplifiers to enhance bandwidth and Litum produced IP67-rated hardware that is durable for harsh working conditions.
