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What is StarMine?

Uncover fresh investment opportunities, streamline your efforts, and concentrate on the most advantageous options available. Integrating StarMine into your investment plan is like augmenting your team with the knowledge and skills of a complete research division made up of experts holding PhDs. Our extensive array of quantitative models and analytics tackles vital elements such as value, momentum, ownership, risk, and quality. Leveraging StarMine’s insights based on data allows you to improve both the speed and quality of your investment choices, resulting in a significantly more effective stock selection process. For example, SmartEstimates emphasizes the most recent forecasts from leading analysts, allowing you to accurately predict future earnings and anticipate shifts in analyst opinions. Moreover, StarMine gives you the tools to investigate novel techniques for your investment strategies, rigorously evaluate your investment hypotheses, and compare your quantitative methods against industry standards, ensuring a comprehensive approach to investing. This versatile tool not only simplifies your decision-making process but also enhances your grasp of market dynamics, ultimately leading to more informed investment outcomes. Additionally, with a focus on continuous improvement, StarMine helps you stay ahead of market trends and better align your strategies with shifting economic conditions.

What is SAS Text Miner?

SAS Text Miner facilitates the extraction of valuable insights from diverse text documents, uncovering hidden themes and concepts. This tool adeptly combines quantitative information with unstructured text, effectively blending text mining with traditional data mining techniques. Being a part of the SAS® Enterprise Miner suite, it requires that SAS Enterprise Miner is installed on the same system to function properly. Furthermore, SAS High-Performance Text Mining can run on both a grid of computers or a single machine with multiple CPUs, making it flexible for various computing environments. The text algorithms used are optimized for multi-threading and operate in memory, which greatly improves both speed and efficiency while reducing input/output load. Users can access SAS Text Miner as nodes within the SAS High-Performance Data Mining framework or through the procedures PROC HPTMINE and PROC HPTMSCORE. To better understand SAS technology, individuals can take advantage of training courses provided by analytics experts, which will help them attain a thorough grasp of the available tools. Gaining expertise in these areas not only boosts one’s analytical skills but also enhances overall capabilities in data mining and analysis methodologies. Ultimately, mastering these techniques can empower users to make more informed decisions based on data-driven insights.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

LSEG Data & Analytics

Company Location

United Kingdom

Company Website

www.lseg.com/en/data-analytics/products/starmine-financial-modeling

Company Facts

Organization Name

SAS Institute

Date Founded

1976

Company Location

United States

Company Website

support.sas.com/en/software/text-miner-support.html

Categories and Features

Categories and Features

Text Mining

Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering

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