SAS Text Miner Description
SAS Text Miner allows for the extraction of insights from a variety of text documents, revealing underlying themes and concepts. This tool effectively integrates quantitative data with unstructured text, merging text mining with conventional data mining approaches. As part of the SAS® Enterprise Miner suite, it necessitates that SAS Enterprise Miner is installed on the same system. Additionally, SAS High-Performance Text Mining can operate on either a computer grid or a single machine equipped with multiple CPUs. The text algorithms employed are designed to be multi-threaded and work in-memory, significantly enhancing both responsiveness and concurrency while minimizing input/output strain. Users can access SAS Text Miner as nodes within the SAS High-Performance Data Mining framework or utilize it through the procedures PROC HPTMINE and PROC HPTMSCORE. To quickly grasp SAS technology, individuals can benefit from courses offered by analytics professionals, ensuring they gain a comprehensive understanding of the tools available. Enhancing one’s knowledge in this area can lead to greater proficiency in data analysis and mining techniques.
SAS Text Miner Alternatives
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RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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NetOwl TextMiner
NetOwl TextMiner merges the acclaimed NetOwl Extractor with Elasticsearch to deliver an innovative text analytics solution. This software harnesses the full spectrum of NetOwl's functionalities, making it perfect for conducting "what if" analyses, performing discovery tasks, facilitating quick-response investigations, and carrying out thorough research. By incorporating all the text analytics features of the NetOwl Extractor—including entity extraction, relationship and event extraction, sentiment analysis, text categorization, and geotagging—TextMiner presents a comprehensive text mining platform. The results generated by the Extractor are stored within Elasticsearch, which offers a range of intelligent search and analytical capabilities. The synergy between Elasticsearch and NetOwl ensures rapid and scalable real-time text analysis suited for handling Big Data. Furthermore, the user-friendly web-based interface of TextMiner can be easily configured to accommodate various analytical needs, enabling users to swiftly access only the most valuable insights from extensive text datasets. This adaptability not only enhances usability but also allows for more tailored analysis across multiple domains.
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PolyAnalyst
PolyAnalyst, a data analysis tool, is used by large companies in many industries (Insurance Manufacturing, Finance, etc.). It uses a visual composer to simplify complex data analysis modeling instead of programming/coding. This is one of its most distinctive features. It can combine structured and poly-structured data for unified analysis (multiple-choice questions and open ended responses), and it can process text data from over 16+ languages. PolyAnalyst provides many features to meet comprehensive data analysis requirements, including the ability to load data, cleanse and prepare data for analysis, deploy machine learning and supervised analytics techniques, and create reports that non-analysts may use to uncover insights.
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Integrations
No Integrations at this time
Company Details
Company:
SAS Institute
Year Founded:
1976
Headquarters:
United States
Website:
support.sas.com/en/software/text-miner-support.html
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Webinars
In Person
Training Videos
Customer Support
Business Hours
Online Support
SAS Text Miner Features and Options
Text Mining Software
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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