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KNIME Text Processing
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KNIME Text Processing

Overview
Synopsis

The KNIME Text Processing feature was designed and developed to read and process textual data, and transform it into numerical data (document and term vectors) in order to apply regular KNIME data mining nodes (e.g. for clustering and classification).

Category

Text Analytics Software Free

Features

• Natural language processing (NLP)
• Text mining
• Information retrieval.

License

Open Source

Price

• KNIME Analytics Platform - Open Source and Free
• KNIME TeamSpace-2'000€ per user/year
• KNIME Server Lite- 7'500€ for 5 users/year
• KNIME WebPortal- 12'500€ for 5 users/year
• KNIME Server-21'000€ for 5 users*/year

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Company

KNIME Text Processing

What is best?

• Natural language processing (NLP)
• Text mining
• Information retrieval.

What are the benefits?

• Enables to read, process, mine and visualize textual data in a convenient way

PAT Rating™ ( Beta)
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
7.6
8.1
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
8.9
Performance
7.6
6.4
Customer Support
7.6
7.6
Implementation
8.5
Renew & Recommend
8.9
Bottom Line

Knime Text processing feature reads and processes textual data and transforms it to numerical data.It achieves this with its text mining capabilities,information retrieval and natural language processing

7.6
Editor Rating
8.1
Aggregated User Rating
2 ratings
You have rated this

The KNIME Text Processing feature was designed and developed to read and process textual data, and transform it into numerical data (document and term vectors) in order to apply regular KNIME data mining nodes (e.g. for clustering and classification). This feature allows for the parsing of texts available in various formats (e.g. Xml, Microsoft Word or PDF and the internal representation of documents and terms) as KNIME data cells stored in a data table. It is possible to recognize and tag different kinds of named entities such as names of persons and organizations, genes and proteins or chemical compounds, thus enriching the documents semantically. Furthermore, documents can be filtered (e.g. by stop word or named entity filters), stemmed by stemmers for various languages and preprocessed in many other ways. Frequencies of words can be computed, keywords can be extracted, and documents can be visualized (e.g.tag clouds). To apply regular KNIME nodes to cluster or classify documents, they can be transformed into numerical vectors. To process texts with the KNIME Text Processing feature, usually six different steps need to be accomplished. These steps are: • IO -Text parsing occurs here;The text and the structure, if available,are extracted and represented in a data structure the Text Processing nodes are able to handle

• Enrichment -semantic information is added by named entity recognition and tagging
• Preprocessing- terms are filtered and manipulated in order to get rid of terms that do not contain content. Terms such as stop words, numbers, punctuation marks or very small words are filtered.
• Frequencies -After preprocessing is finished, frequencies of terms in documents and the complete corpus can be computed
• Transformation- The textual data is first transformed into numerical data before regular KNIME nodes are applied on the documents.
• Visualization - There are two nodes that visualize textual data, the “ Document Viewer ” and the “ Tag Cloud ” node. The “Document Viewer” node allows for a simple visualization of documents. The “Tag Cloud” node creates a typical tag cloud with additional options such as different arrangements of terms.

 

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Ease of use
Features & Functionality
Advanced Features
Integration
Performance
Customer Support
Implementation
Renew & Recommend

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