Text Analytics 24 Success Secrets - 24 Most Asked Questions On Text Analytics - What You Need To Know

Business & Finance

• Author: Benjamin Rosales
• ISBN: 9781488535383
• Publisher: Emereo Publishing
• Publication: February 3, 2014
• Imprint: Emereo Publishing
• Language: English

Text mining', as well referenced to like written material information mining, approximately equal to '#Text excavating and written material analytics|text analytics', alludes to the procedure of deriving high-quality data as of Plain text. High-quality data is characteristically obtained via the conceiving of models and tendencies via intents such like design recognition|statistical design educating. Text excavating normally includes the procedure of arranging the input written material (usually parsing, alongside with the extension of a few obtained lexical attributes and the deletion of other ones, and following introduction in to a database), deriving models inside the organized information, and eventually assessment and explanation of the yield. 'high quality' in written material excavating normally alludes to a few amalgamation of relevancy (information retrieval)|relevance, freshness (patent)|novelty, and interestingness.

There has never been a Text Analytics Guide like this.

It contains 24 answers, much more than you can imagine; comprehensive answers and extensive details and references, with insights that have never before been offered in print. Get the information you need--fast! This all-embracing guide offers a thorough view of key knowledge and detailed insight. This Guide introduces what you want to know about Text Analytics.

A quick look inside of some of the subjects covered: Intelligent text analysis - Commercial, Analytics - Analytics vs. analysis, Data mining Commercial data-mining software and applications, Text mining - Commercial, Text mining - History, Semantic analytics, Data analysis, Angoss - Services, Sentiment analysis, Unstructured information - Dealing with unstructured data, SPSS Modeler, Intelligent text analysis - Text mining and text analytics, Customer intelligence Example sources of data for CI, Data extraction, Predictive analytics Technology and big data influences, and much more...