Text mining in practice with R / Ted Kwartler.
By: Kwartler, Ted [author.]
Language: English Publisher: Hoboken, NJ : John Wiley & Sons, 2017Description: 1 online resource (320 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119282099 (pdf); 9781119282082 (epub)Subject(s): Data mining | Text processing (Computer science)Genre/Form: Electronic books.DDC classification: 006.3/12 LOC classification: QA76.9.D343Online resources: Full text available at Wiley Online Library Click here to viewItem type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY LIC Gateway | 006.312 K9794 2017 (Browse shelf) | Available | 50397 |
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006.31 L459 2021 Machine Learning for Time Series Forecasting With Python / | 006.31 R1801 2019 Keras to Kubernetes : the journey of a machine learning model to production / | 006.312 K1391 2011 Data mining : concepts, models, methods, and algorithms / | 006.312 K9794 2017 Text mining in practice with R / | 006.312 L3278 2019 Data science using Python and R / | 006.312 M9909 2014 Making sense of data I : a practical guide to exploratory data analysis and data mining / | 006.312 R137 2012 Mining of massive datasets / |
Includes index.
What is text mining? -- Basics of text mining -- Common text mining visualizations -- Sentiment scoring -- Hidden structures : clustering, string distance, text vectors & topic modeling -- Document classification : finding clickbait from headlines -- Predictive modeling : using text for classifying & predicting outcomes -- The OpenNLP Project -- Text sources.
A reliable, cost-effective approach to extracting priceless business information from all sources of text
Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R.
Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You’ll learn how to:
Identify actionable social media posts to improve customer service
Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more
Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files
Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more
Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.
600-699 620
Description based on print version record and CIP data provided by publisher.
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