Download or read book Text Analysis and Representation written by Ian Cushing and published by Cambridge University Press. This book was released on 2018-01-25 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential study guides for the future linguist. Text Analysis and Representation is a general introduction to the methods and principles behind English linguistics study, suitable for students at advanced level and beyond. Written with input from the Cambridge English Corpus, it looks at the way meaning is made using authentic written and spoken examples. This helps students give confident analysis and articulate responses. Using short activities to help explain analysis methods, this book guides students through major modern issues and concepts. It summarises key concerns and modern findings, while providing inspiration for language investigations and non-examined assessments (NEAs) with research suggestions.
Download or read book Text Representation written by Ted Sanders and published by John Benjamins Publishing. This book was released on 2001-12-19 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together linguistics and psycholinguistics. Text representation is considered a cognitive entity: a mental construct that plays a crucial role in both text production and text understanding. The focus is on referential and relational coherence and the role of linguistic characteristics as processing instructions from a text linguistic and discourse psychology point of view. Consequently, this book presents various research methodologies: linguistic analysis, text analysis, corpus linguistics, computational linguistics, argumentation analysis, and the experimental psycholinguistic study of text processing. The authors compare, test, and evaluate linguistic and processing theories of text representation. A state of the art volume in an emerging field of interest, located at the very heart of our communicative behavior: the study of text and text representation.
Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Download or read book Representations of Poverty and Place written by Laura L Paterson and published by Springer. This book was released on 2018-11-03 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores a novel methodological approach which combines analytical techniques from linguistics and geography to bring fresh insights to the study of poverty. Using Geographical Text Analysis, it maps the discursive construction of poverty in the UK and compares the results to what administrative data reveal. The analysis draws together qualitative and quantitative techniques from corpus linguistics, critical discourse analysis, Geographical Information Science, and the spatial humanities. By identifying the place-names that occur within close proximity to search terms associated with to poverty it shows how different newspapers use place to foreground different aspects of poverty (including employment, housing, money, and benefits), and how the London-centric nature of newspaper reporting dominates the discursive construction of UK poverty. This book demonstrates how interdisciplinary research methods can illuminate complex social issues and will appeal to researchers in a number of disciplines from sociology, geography and the spatial humanities, economics, linguistics, health, and public policy, in addition to policymakers and practitioners.
Download or read book Practical Text Mining and Statistical Analysis for Non structured Text Data Applications written by Gary Miner and published by Academic Press. This book was released on 2012-01-11 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--
Download or read book Text as Data written by Justin Grimmer and published by Princeton University Press. This book was released on 2022-03-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
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 328 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
Download or read book Text Analysis and Representation written by Ian Cushing and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Written Text Analysis written by Malcolm Coulthard and published by Routledge. This book was released on 2002-11-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides an overview of a wide range of approaches to written text analysis. It includes both classic and specially commissioned papers by distinguished authors, which share a common linguistic framework. The pieces contain a variety of focuses from the patterning of paragraphs, sections or whole texts to the organization of clauses, individual expressions and single words, as well as a variety of text-types. The examples used range from pure science through social science, academic journals, weekly magazines and newspapers, to literary narratives. This collection forms the basis for an course on written text analysis that should be of interest to advanced undergraduate and postgraduate students.
Download or read book Qualitative Text Analysis written by Udo Kuckartz and published by SAGE. This book was released on 2014-01-23 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can you analyse narratives, interviews, field notes, or focus group data? Qualitative text analysis is ideal for these types of data and this textbook provides a hands-on introduction to the method and its theoretical underpinnings. It offers step-by-step instructions for implementing the three principal types of qualitative text analysis: thematic, evaluative, and type-building. Special attention is paid to how to present your results and use qualitative data analysis software packages, which are highly recommended for use in combination with qualitative text analysis since they allow for fast, reliable, and more accurate analysis. The book shows in detail how to use software, from transcribing the verbal data to presenting and visualizing the results. The book is intended for Master’s and Doctoral students across the social sciences and for all researchers concerned with the systematic analysis of texts of any kind.
Download or read book Natural Language Processing and Text Mining written by Anne Kao and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.
Download or read book Fourier Analysis on Finite Groups and Applications written by Audrey Terras and published by Cambridge University Press. This book was released on 1999-03-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: It examines the theory of finite groups in a manner that is both accessible to the beginner and suitable for graduate research.
Download or read book Handbook of Research on Emerging Trends and Applications of Machine Learning written by Solanki, Arun and published by IGI Global. This book was released on 2019-12-13 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
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
Download or read book Textual Analysis written by Alan McKee and published by SAGE. This book was released on 2003-04-03 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Textual analysis is a methodology - a way of gathering data - for researchers who are interested in the ways in which people make sense of the world.
Download or read book Representation and the Text written by William G. Tierney and published by SUNY Press. This book was released on 1997-01-01 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on authorial representations of contested reality in qualitative research.This book focuses on representations of contested realities in qualitative research. The authors examine two separate, but interrelated, issues: criticisms of how researchers use "voice," and suggestions about how to develop experimental voices that expand the range of narrative strategies. Changing relationships between researchers and respondents dictate alterations in textual representations--from the "view from nowhere" to the view from a particular location, and from the omniscient voice to the polyvocality of communities of individuals. Examples of new representations and textual experiments provide models for how some authors have struggled with voice in their texts, and in so doing, broaden who they and we mean by "us."
Download or read book Text Analytics with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2019-05-21 with total page 688 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. 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.
