Applied Text Analysis With Python

Download Applied Text Analysis With Python full books in PDF, epub, and Kindle. Read online Applied Text Analysis With Python ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Applied Text Analysis with Python

Author : Benjamin Bengfort
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491962992
Total Pages : 332 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Applied Text Analysis with Python by : Benjamin Bengfort

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Applied Text Mining

Author : Usman Qamar
Publisher : Springer
ISBN 13 : 9783031519161
Total Pages : 0 pages
Book Rating : 4.5/5 (191 download)

DOWNLOAD NOW!

Book Synopsis Applied Text Mining by : Usman Qamar

Download or read book Applied Text Mining written by Usman Qamar and published by Springer. This book was released on 2024-04-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP). It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, including models for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.

Text Analysis with Python: A Research Oriented Guide

Author : Mamta Mittal
Publisher : Bentham Science Publishers
ISBN 13 : 9815049615
Total Pages : 268 pages
Book Rating : 4.8/5 (15 download)

DOWNLOAD NOW!

Book Synopsis Text Analysis with Python: A Research Oriented Guide by : Mamta Mittal

Download or read book Text Analysis with Python: A Research Oriented Guide written by Mamta Mittal and published by Bentham Science Publishers. This book was released on 2022-08-12 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.

Text Analytics with Python

Author : Dipanjan Sarkar
Publisher :
ISBN 13 : 9781484243558
Total Pages : 674 pages
Book Rating : 4.2/5 (435 download)

DOWNLOAD NOW!

Book Synopsis Text Analytics with Python by : Dipanjan Sarkar

Download or read book Text Analytics with Python written by Dipanjan Sarkar and published by . This book was released on 2019 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. -- Also the key selling points • Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP • Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP • Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.

Blueprints for Text Analytics Using Python

Author : Jens Albrecht
Publisher :
ISBN 13 : 9781492074083
Total Pages : 350 pages
Book Rating : 4.0/5 (74 download)

DOWNLOAD NOW!

Book Synopsis Blueprints for Text Analytics Using Python by : Jens Albrecht

Download or read book Blueprints for Text Analytics Using Python written by Jens Albrecht and published by . This book was released on 2021-01-12 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turning text into valuable information is essential for many businesses looking to gain a competitive advantage. There have many improvements in natural language processing and users have a lot of options when choosing to work on a problem. However, it's not always clear which NLP tools or libraries would work for a business use--or which techniques you should use and in what order. This practical book provides theoretical background and real-world case studies with detailed code examples to help developers and data scientists obtain insight from text online. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler use blueprints for text-related problems that apply state-of-the-art machine learning methods in Python. If you have a fundamental understanding of statistics and machine learning along with basic programming experience in Python, you're ready to get started. You'll learn how to: Crawl and clean then explore and visualize textual data in different formats Preprocess and vectorize text for machine learning Apply methods for classification, topic analysis, summarization, and knowledge extraction Use semantic word embeddings and deep learning approaches for complex problems Work with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch

Blueprints for Text Analytics Using Python

Author : Jens Albrecht
Publisher : O'Reilly Media
ISBN 13 : 1492074055
Total Pages : 422 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Blueprints for Text Analytics Using Python by : Jens Albrecht

Download or read book Blueprints for Text Analytics Using Python written by Jens Albrecht and published by O'Reilly Media. This book was released on 2020-12-04 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Natural Language Processing and Computational Linguistics

Author : Bhargav Srinivasa-Desikan
Publisher : Packt Publishing Ltd
ISBN 13 : 1788837037
Total Pages : 298 pages
Book Rating : 4.7/5 (888 download)

DOWNLOAD NOW!

Book Synopsis Natural Language Processing and Computational Linguistics by : Bhargav Srinivasa-Desikan

Download or read book Natural Language Processing and Computational Linguistics written by Bhargav Srinivasa-Desikan and published by Packt Publishing Ltd. This book was released on 2018-06-29 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!

Text Analytics with Python

Author : Dipanjan Sarkar
Publisher : Apress
ISBN 13 : 1484223888
Total Pages : 397 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis Text Analytics with Python by : Dipanjan Sarkar

Download or read book Text Analytics with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2016-11-30 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Build a 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 Analysis with Python

Author : Mamta Mittal; Gopi
Publisher :
ISBN 13 : 9789815049626
Total Pages : 0 pages
Book Rating : 4.0/5 (496 download)

DOWNLOAD NOW!

Book Synopsis Text Analysis with Python by : Mamta Mittal; Gopi

Download or read book Text Analysis with Python written by Mamta Mittal; Gopi and published by . This book was released on 2022-08-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.

Text Analysis in Python for Social Scientists

Author : Dirk Hovy
Publisher : Cambridge University Press
ISBN 13 : 1108963099
Total Pages : pages
Book Rating : 4.1/5 (89 download)

DOWNLOAD NOW!

Book Synopsis Text Analysis in Python for Social Scientists by : Dirk Hovy

Download or read book Text Analysis in Python for Social Scientists written by Dirk Hovy and published by Cambridge University Press. This book was released on 2022-03-31 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no limit to the kinds of things we can predict from text: power, trust, misogyny, are all signaled in language. These algorithms easily scale to corpus sizes infeasible for manual analysis. Prediction algorithms have become steadily more powerful, especially with the advent of neural network methods. However, applying these techniques usually requires profound programming knowledge and machine learning expertise. As a result, many social scientists do not apply them. This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.

Blueprints for Text Analytics Using Python

Author : Jens Albrecht
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492074039
Total Pages : 504 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Blueprints for Text Analytics Using Python by : Jens Albrecht

Download or read book Blueprints for Text Analytics Using Python written by Jens Albrecht and published by "O'Reilly Media, Inc.". This book was released on 2020-12-04 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : John Atkinson-Abutridy
Publisher : CRC Press
ISBN 13 : 1000581071
Total Pages : 201 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Text Analytics by : John Atkinson-Abutridy

Download or read book Text Analytics written by John Atkinson-Abutridy and published by CRC Press. This book was released on 2022-05-03 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Python Text Mining

Author : Alexandra George
Publisher : BPB Publications
ISBN 13 : 9389898781
Total Pages : 342 pages
Book Rating : 4.3/5 (898 download)

DOWNLOAD NOW!

Book Synopsis Python Text Mining by : Alexandra George

Download or read book Python Text Mining written by Alexandra George and published by BPB Publications. This book was released on 2022-03-26 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make use of the most advanced machine learning techniques to perform NLP and feature extraction KEY FEATURES ● Learn about pre-trained models, deep learning, and transfer learning for NLP applications. ● All-in-one knowledge guide for feature engineering, NLP models, and pre-processing techniques. ● Includes use cases, enterprise deployments, and a range of Python based demonstrations. DESCRIPTION Natural Language Processing (NLP) has proven to be useful in a wide range of applications. Because of this, extracting information from text data sets requires attention to methods, techniques, and approaches. 'Python Text Mining' includes a number of application cases, demonstrations, and approaches that will help you deepen your understanding of feature extraction from data sets. You will get an understanding of good information retrieval, a critical step in accomplishing many machine learning tasks. We will learn to classify text into discrete segments solely on the basis of model properties, not on the basis of user-supplied criteria. The book will walk you through many methodologies, such as classification, that will enable you to rapidly construct recommendation engines, subject segmentation, and sentiment analysis applications. Toward the end, we will also look at machine translation and transfer learning. By the end of this book, you'll know exactly how to gather web-based text, process it, and then apply it to the development of NLP applications. WHAT YOU WILL LEARN ● Practice how to process raw data and transform it into a usable format. ● Best techniques to convert text to vectors and then transform into word embeddings. ● Unleash ML and DL techniques to perform sentiment analysis. ● Build modern recommendation engines using classification techniques. WHO THIS BOOK IS FOR This book is a good place to start with examples, explanations, and exercises for anyone interested in learning more about advanced text mining and natural language processing techniques. It is suggested but not required that you have some prior programming experience. TABLE OF CONTENTS 1. Basic Text Processing Techniques 2. Text to Numbers 3. Word Embeddings 4. Topic Modeling 5. Unsupervised Sentiment Classification 6. Text Classification Using ML 7. Text Classification Using Deep learning 8. Recommendation engine 9. Transfer Learning

Introduction to Python Programming for Business and Social Science Applications

Author : Frederick Kaefer
Publisher : SAGE Publications
ISBN 13 : 1544377487
Total Pages : 542 pages
Book Rating : 4.5/5 (443 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Python Programming for Business and Social Science Applications by : Frederick Kaefer

Download or read book Introduction to Python Programming for Business and Social Science Applications written by Frederick Kaefer and published by SAGE Publications. This book was released on 2020-08-06 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Would you like to gather big datasets, analyze them, and visualize the results, all in one program? If this describes you, then Introduction to Python Programming for Business and Social Science Applications is the book for you. Authors Frederick Kaefer and Paul Kaefer walk you through each step of the Python package installation and analysis process, with frequent exercises throughout so you can immediately try out the functions you’ve learned. Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis. Instead of teaching you the principles and practices of programming as a whole, this application-oriented text focuses on only what you need to know to research and answer social science questions. The text features two types of examples, one set from the General Social Survey and one set from a large taxi trip dataset from a major metropolitan area, to help readers understand the possibilities of working with Python. Chapters on installing and working within a programming environment, basic skills, and necessary commands will get you up and running quickly, while chapters on programming logic, data input and output, and data frames help you establish the basic framework for conducting analyses. Further chapters on web scraping, statistical analysis, machine learning, and data visualization help you apply your skills to your research. More advanced information on developing graphical user interfaces (GUIs) help you create functional data products using Python to inform general users of data who don’t work within Python. First there was IBM® SPSS®, then there was R, and now there′s Python. Statistical software is getting more aggressive - let authors Frederick Kaefer and Paul Kaefer help you tame it with Introduction to Python Programming for Business and Social Science Applications.

Learning Data Science

Author : Sam Lau
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098112970
Total Pages : 597 pages
Book Rating : 4.0/5 (981 download)

DOWNLOAD NOW!

Book Synopsis Learning Data Science by : Sam Lau

Download or read book Learning Data Science written by Sam Lau and published by "O'Reilly Media, Inc.". This book was released on 2023-09-15 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data

Data Analytics Applications in Emerging Markets

Author : José Antonio Núñez Mora
Publisher : Springer Nature
ISBN 13 : 9811946957
Total Pages : 209 pages
Book Rating : 4.8/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Data Analytics Applications in Emerging Markets by : José Antonio Núñez Mora

Download or read book Data Analytics Applications in Emerging Markets written by José Antonio Núñez Mora and published by Springer Nature. This book was released on 2022-10-26 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book analyzes the impact of technology in emerging markets by considering conditions and the history of how it has changed the way of working and market development in such contexts. The book delves into key areas such as fintech enterprises, artificial intelligence, pension funds, stock markets, and energy markets though applied studies and research. This book is a useful read for practitioners and scholars interested in how technology has and continues to change the way in which development is defined and achieved, particularly in emerging markets.

Text Analytics with Python

Author : Anthony S. Williams
Publisher : Anthony S. Williams
ISBN 13 :
Total Pages : 108 pages
Book Rating : 4./5 ( download)

DOWNLOAD NOW!

Book Synopsis Text Analytics with Python by : Anthony S. Williams

Download or read book Text Analytics with Python written by Anthony S. Williams and published by Anthony S. Williams. This book was released on 2020-07-13 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!