Text Analytics

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Text Analytics With Python

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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2016-11-30

Text Analytics With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-30 with Computers categories.

Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Builda text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data

Text Mining With R

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Author : Julia Silge
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-12

Text Mining With R written by Julia Silge and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

Practical Text Analytics

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Author : Murugan Anandarajan
language : en
Publisher: Springer
Release Date : 2018-10-19

Practical Text Analytics written by Murugan Anandarajan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Business & Economics categories.

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.

Text Analytics

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Author : Domenica Fioredistella Iezzi
language : en
Publisher: Springer Nature
Release Date : 2020-11-24

Text Analytics written by Domenica Fioredistella Iezzi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-24 with Social Science categories.

Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing.

Text Analytics

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Author : John Atkinson-Abutridy
language : en
Publisher: CRC Press
Release Date : 2022-05-03

Text Analytics written by John Atkinson-Abutridy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-03 with Computers categories.

Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources. Features: Easy-to-follow step-by-step concepts and methods Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves Practical programming exercises in Python for each chapter Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797

Applied Text Analysis With Python

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Author : Benjamin Bengfort
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-06-11

Applied Text Analysis With Python written by Benjamin Bengfort and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-11 with Computers categories.

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Blueprints For Text Analytics Using Python

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Author : Jens Albrecht
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-12-04

Blueprints For Text Analytics Using Python written by Jens Albrecht and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.

Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order. This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly. Extract data from APIs and web pages Prepare textual data for statistical analysis and machine learning Use machine learning for classification, topic modeling, and summarization Explain AI models and classification results Explore and visualize semantic similarities with word embeddings Identify customer sentiment in product reviews Create a knowledge graph based on named entities and their relations

Text Analytics With Sas

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Author :
language : en
Publisher:
Release Date : 2019-06-14

Text Analytics With Sas written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-14 with categories.

SAS provides many different solutions to investigate and analyze text and operationalize decisioning. Several impressive papers have been written to demonstrate how to use these techniques. We have carefully selected a handful of these from recent Global Forum contributions to introduce you to the topic and let you sample what each has to offer. Also available free as a PDF from sas.com/books.

Introduction To Text Analytics

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Author : Emily Ohman
language : en
Publisher: SAGE Publications Limited
Release Date : 2024-11-30

Introduction To Text Analytics written by Emily Ohman and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-30 with Reference categories.

Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.

Text Analytics With Python

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Author : Anthony S. Williams
language : en
Publisher: Anthony S. Williams
Release Date : 2020-07-13

Text Analytics With Python written by Anthony S. Williams and has been published by Anthony S. Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.

Text Analytics with Python Text analytics is all about obtaining relevant and useful information from some unstructured data. Text analytics techniques can be of great importance and can provide amazing help for various organizations that aim to derive some potentially valuable business insights from an amazingly large collection of text-based content like social media streams, emails or word documents. Sure, text analytics using natural language processing, machine learning, and statistical modeling can be very challenging since human language is commonly inconsistent. It contains various ambiguities mainly caused by inconsistent semantics and syntax. Fortunately, text analytics software can easily help you by transposing phrases and words contained in unstructured data into some numerical values that you later link with structured data contained in data set. It is more than apparent that major enterprises are increasingly and rapidly turning to text analytics techniques in order to improve their businesses as well as overall customer satisfaction. We are witnessing that amazing variety and volume when it comes to data generated across different feedback channels which continues to grow and expand providing various businesses with a wealth of valuable information regarding their customers. It is more than apparent that sifting through all available content would be amazingly time-consuming to be done manually. However, understanding those insights held in data is more than critical when it comes to getting an accurate view of the customer's voice. We are also witnessing the next chapter of text analytics approach since it's already developing that solid ground. It will also continue to be among other technical necessities today and into the future. In order to keep up with the future, embark on your own text analytics journey having this book by your side as your best companion. In this book ou will learn: Text analytics process How to build a corpus and analyze sentiment Named entity extraction with Groningen meaning bank corpus How to train your system Getting started with NLTK How to search syntax and tokenize sentences Automatic text summarization Stemming word and topic modeling with NLTK Using scikit-learn for text classification Part of speech tagging and POS tagging models in NLTK And much, much more... Get this book NOW and learn more about Text Analytics with Python!