Text Analytics With Python

Text Analytics with Python PDF
Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484243544
Size: 44.49 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 674
View: 3927

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Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

Applied Text Analysis With Python

Applied Text Analysis with Python PDF
Author: Benjamin Bengfort
Publisher: "O'Reilly Media, Inc."
ISBN: 1491962992
Size: 34.42 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 332
View: 3777

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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

Text Analytics With Python

Text Analytics with Python PDF
Author: Anthony S. Williams
Publisher: Anthony S. Williams
ISBN:
Size: 39.24 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 107
View: 610

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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!

Modeling Techniques In Predictive Analytics With Python And R

Modeling Techniques in Predictive Analytics with Python and R PDF
Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Size: 75.88 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 448
View: 6258

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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Blueprints For Text Analytics Using Python

Blueprints for Text Analytics Using Python PDF
Author: Jens Albrecht
Publisher: O'Reilly Media
ISBN: 1492074055
Size: 34.16 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 424
View: 3763

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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 With Python Cookbook

Natural Language Processing with Python Cookbook PDF
Author: Krishna Bhavsar
Publisher: Packt Publishing Ltd
ISBN: 178728347X
Size: 36.63 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 316
View: 5748

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Learn the tricks and tips that will help you design Text Analytics solutions About This Book Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python Use dictionaries to create your own named entities using this easy-to-follow guide Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing Who This Book Is For This book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended. What You Will Learn Explore corpus management using internal and external corpora Learn WordNet usage and a couple of simple application assignments using WordNet Operate on raw text Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more Understand regular expressions for pattern matching Learn to use and write your own POS taggers and grammars Learn to evaluate your own trained models Explore Deep Learning techniques in NLP Generate Text from Nietzsche's writing using LSTM Utilize the BABI dataset and LSTM to model episodes In Detail Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. Style and Approach This book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.

           PDF
Author: فرانتس كافكا
Publisher: Al Manhal
ISBN:
Size: 66.34 MB
Format: PDF, ePub, Docs
Category : Literary Collections
Languages : ar
Pages : 80
View: 2256

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تعد رواية "المسخ" للكاتب "فرانز كافكا" من نوعية الروايات التي يجب أن تتعب نفسك وتجتهد لتلحظ جمالها، وهي رواية أسلوبها رائع وعميقة المعاني، ولا شك أن القصة وا?حداث غريبة جدا، وتبدو سوريالية الى حد بعيد، لكنها واقعية، ومن أحداث الراوية: (تخيل أن تستيقظ من النوم، لتجد نفسك و قد تحولت إلى حشرة، بهذه الطريقة يفتتح كافكا روايته المسخ، غريغور سامسا، بطل الرواية، يستيقظ في أحد ا?يام ليكتشف تحوله إلى حشرة، هذه الحشرة غير محددة النوع، تحمل صفات الصرصار والخنفساء، يعمل غريغور كبائع متجول، مما يجعله مصدر دخل ?سرته و العائل الوحيد بعد مرض والدته، و توقف والده عن العمل، و محاولة غريغور تسديد ديون والده المريضة بالربو، إضافة إلى أخته اللماحة المقربة. يستيقظ غريغور داخل حجرته المقفلة ا?بواب-حسب عادته في إغلاق غرفته ليلا ويلاحظ من طريقة تمدده ومظهر أعضائه أنه قد تحول إلى حشرة، تتوالى ا?حداث في الرواية، لتصف وضع غريغور داخل غرفته وغربته داخل عائلته، و شفقة هذه العائلة عليه، ثم تقززها ورغبتها في الخلاص منه بهيئته المقززة، لتأتي النهاية متوقعة وصادمة في الوقت نفس..).

Learning Data Mining With Python

Learning Data Mining with Python PDF
Author: Robert Layton
Publisher: Packt Publishing Ltd
ISBN: 1784391204
Size: 78.84 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 344
View: 6233

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The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.