Advertisement
| example of a text analysis: Text Analysis with R Matthew L. Jockers, Rosamond Thalken, 2020-03-30 Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms. |
| example of a text analysis: About Writing Robin Jeffrey, 2016 |
| example of a text analysis: Text Analysis in Translation Christiane Nord, 2006-01-01 Text Analysis in Translation has become a classic in Translation Studies. Based on a functional approach to translation and endebted to pragmatic text linguistics, it suggests a model for translation-oriented source-text analysis applicable to all text types and genres independent of the language and culture pairs involved. Part 1 of the study presents the theoretical framework on which the model is based, and surveys the various concepts of translation theory and text linguistics. Part 2 describes the role and scope of source-text analysis in the translation process and explains why the model is relevant to translation. Part 3 presents a detailed study of the extratextual and intratextual factors and their interaction in the text, using numerous examples from all areas of professional translation. Part 4 discusses the applications of the model to translator training, placing particular emphasis on the selection of material for translation classes, grading the difficulty of translation tasks, and translation quality assessment. The book concludes with the practical analysis of a number of texts and their translations, taking into account various text types and several languages (German, English, Spanish, French, Italian, Portuguese, and Dutch). |
| example of a text analysis: Text Mining with R Julia Silge, David Robinson, 2017-06-12 Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling. |
| example of a text analysis: Qualitative Text Analysis Udo Kuckartz, 2014-01-23 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. |
| example of a text analysis: Textual Analysis Alan McKee, 2003-04-03 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. |
| example of a text analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| example of a text analysis: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| example of a text analysis: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications Gary Miner, 2012-01-11 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-- |
| example of a text analysis: Digital Humanities Pedagogy Brett D. Hirsch, 2012 The essays in this collection offer a timely intervention in digital humanities scholarship, bringing together established and emerging scholars from a variety of humanities disciplines across the world. The first section offers views on the practical realities of teaching digital humanities at undergraduate and graduate levels, presenting case studies and snapshots of the authors' experiences alongside models for future courses and reflections on pedagogical successes and failures. The next section proposes strategies for teaching foundational digital humanities methods across a variety of scholarly disciplines, and the book concludes with wider debates about the place of digital humanities in the academy, from the field's cultural assumptions and social obligations to its political visions. (4e de couverture). |
| example of a text analysis: Practical Text Analytics Murugan Anandarajan, Chelsey Hill, Thomas Nolan, 2018-10-19 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. |
| example of a text analysis: Supervised Machine Learning for Text Analysis in R Emil Hvitfeldt, Julia Silge, 2021-10-22 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. |
| example of a text analysis: Multilingual Text Analysis Marina Litvak, Natalia Vanetik, 2019 Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge -- facts, rules, and relationships -- that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains. |
| example of a text analysis: Text Analytics with Python Dipanjan Sarkar, 2019-05-21 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. |
| example of a text analysis: Methods of Text and Discourse Analysis Stefan Titscher, Michael Meyer, Ruth Wodak, Eva Vetter, 2000-07-26 ′This volume is the most comprehensive overview to date of sociologically orientated approaches to text and discourse analysis and is worth reading even for those who are interested only in purely linguistiv approaches to text and discourse. Its main merit, I think, is that it intorduces approaches which up to now have hardley been admitted into the universe of scientific discourse′ - Discourse Studies Methods of Text and Discourse Analysis provides the most comprehensive overview currently available of linguistic and sociological approaches to text and discourse analysis. Among the 10 linguistic and sociological models surveyed in this book some of the more important are Grounded Theory, Content Analysis, Conversation Analysis and Critical Discourse Analysis. The book presents each approach according to a standardised format, which allows for direct systematic comparisons. The fully annotated lists of sources provide readers with an additional means of evaluation of the competing analytical methods. Interdisciplinary and international in its aims, Methods of Text and Discourse Analysis suggests the benefits both linguists and sociologists will derive from a more intimate knowledge of each others′ methods and procedures. |
| example of a text analysis: Applied Text Analysis with Python Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda, 2018-06-11 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 |
| example of a text analysis: Blueprints for Text Analytics Using Python Jens Albrecht, Sidharth Ramachandran, Christian Winkler, 2020-12-04 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 |
| example of a text analysis: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| example of a text analysis: Handbook of Multimethod Measurement in Psychology Michael Eid, Ed Diener, 2006 Provides a single, understandable resource for learning the meaning, advantages, and limitations of different methods. This volume discusses theoretical concepts, then lays out the important measurement tools - including global self-assessment, informant assessment, observational methods, and nonreactive methods, and more |
| example of a text analysis: Film Text Analysis Janina Wildfeuer, John A. Bateman, 2016-10-04 This book examines film as a multimodal text and an audiovisual synthesis, bringing together current work within the fields of narratology, philosophy, multimodal analysis, sound as well as cultural studies in order to cover a wide range of international academic interest. The book provides new insights into current work and turns the discussion towards recent research questions and analyses, representing and constituting in each contribution new work in the discipline of film text analysis. With the help of various example analyses, all showing the methodological applicability of the discussed issues, the collection provides novel ways of considering film as one of the most complex and at the same time broadly comprehensible texts. |
| example of a text analysis: Introducing Electronic Text Analysis Svenja Adolphs, 2006-09-27 Introducing Electronic Text Analysis is a practical and much needed introduction to corpora – bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts which are then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. It will also be supported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216 |
| example of a text analysis: What Writing Does and How It Does It Charles Bazerman, Paul Prior, 2003-12-08 In What Writing Does and How It Does It, editors Charles Bazerman and Paul Prior offer a sophisticated introduction to methods for understanding, studying, and analyzing texts and writing practices. This volume addresses a variety of approaches to analyzing texts, and considers the processes of writing, exploring textual practices and their contexts, and examining what texts do and how texts mean rather than what they mean. Included are traditional modes of analysis (rhetorical, literary, linguistic), as well as newer modes, such as text and talk, genre and activity analysis, and intertextual analysis. The chapters have been developed to provide answers to a specified set of questions, with each one offering: *a preview of the chapter's content and purpose; *an introduction to basic concepts, referring to key theoretical and research studies in the area; *details on the types of data and questions for which the analysis is best used; *examples from a wide-ranging group of texts, including educational materials, student writing, published literature, and online and electronic media; *one or more applied analyses, with a clear statement of procedures for analysis and illustrations of a particular sample of data; and *a brief summary, suggestions for additional readings, and a set of activities. The side-by-side comparison of methods allows the reader to see the multi-dimensionality of writing, facilitating selection of the best method for a particular research question. The volume contributors are experts from linguistics, communication studies, rhetoric, literary analysis, document design, sociolinguistics, education, ethnography, and cultural psychology, and each utilizes a specific mode of text analysis. With its broad range of methodological examples, What Writing Does and How It Does It is a unique and invaluable resource for advanced undergraduate and graduate students and for researchers in education, composition, ESL and applied linguistics, communication, L1 and L2 learning, print media, and electronic media. It will also be useful in all social sciences and humanities that place importance on texts and textual practices, such as English, writing, and rhetoric. |
| example of a text analysis: Text Analysis and Representation Ian Cushing, 2018-01-25 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. |
| example of a text analysis: Handbook of Methodological Approaches to Community-based Research Leonard Jason, David Glenwick, 2016 The Handbook of Methodological Approaches to Community-Based Research is intended to aid the community-oriented researcher in learning about and applying cutting-edge quantitative, qualitative, and mixed methods approaches. |
| example of a text analysis: Beyond Literary Analysis Allison Marchetti, Rebekah O'Dell, 2018 This book will make the case for multiple, diverse kinds of analysis to be taught in the high school English classroom. In addition to showing what written analysis looks like in the wild, the authors will provide readers with a framework of fundamental analytical skills for instruction. Importantly, Marchetti and O'Dell will advocate for framing analytical writing around students' (of all levels and abilities) passions and expertise. And just as they do in their previous Heinemann book, Writing with Mentors, they will share resources for bringing many different kinds of analytical writing into the classroom-- |
| example of a text analysis: From Text to Political Positions Bertie Kaal, Isa Maks, Annemarie van Elfrinkhof, 2014-05-15 From Text to Political Positions addresses cross-disciplinary innovation in political text analysis for party positioning. Drawing on political science, computational methods and discourse analysis, it presents a diverse collection of analytical models including pure quantitative and qualitative approaches. By bringing together the prevailing text-analysis methods from each discipline the volume aims to alert researchers to new and exciting possibilities of text analyses across their own disciplinary boundary. The volume builds on the fact that each of the disciplines has a common interest in extracting information from political texts. The focus on political texts thus facilitates interdisciplinary cross-overs. The volume also includes chapters combining methods as examples of cross-disciplinary endeavours. These chapters present an open discussion of the constraints and (dis)advantages of either quantitative or qualitative methods when evaluating the possibilities of combining analytic tools. |
| example of a text analysis: Digging Into Literature Joanna Wolfe, Laura Wilder, 2015-11-17 Digging Into Literature reveals the critical strategies that any college student can use for reading, analyzing, and writing about literary texts. It is based on a groundbreaking study of the successful interpretive and argumentative moves of more than a thousand professional and student essays. Full of practical charts and summaries, with plenty of exercises and activities for trying out the strategies, the book convincingly reveals that while great literature is profoundly and endlessly complex, writing cogent and effective essays about it doesn’t have to be. |
| example of a text analysis: Textual Analysis Bodil Helder, 2011 |
| example of a text analysis: Textual Analysis Made Easy C. Brian Taylor, 2016-10-04 In this new book, you’ll learn how to teach evidence-based writing using a variety of tools, activities, and sample literary texts. Showing elementary and middle school students how to think critically about what they’re reading can be a challenge, but author C. Brian Taylor makes it easy by presenting twelve critical thinking tools along with step-by-step instructions for implementing each one effectively in the classroom. You’ll learn how to: Design units and lesson plans that gradually introduce your students to more complex levels of textual analysis; Encourage students to dig deeper by using the 12 Tools for Critical Thinking; Help students identify context and analyze quotes with the Evidence Finder graphic organizer; Use the Secret Recipe strategy to construct persuasive evidence-based responses that analyze a text’s content or technique; Create Cue Cards to teach students how to recognize and define common literary devices. The book also offers a series of extra examples using mentor texts, so you can clearly see how the strategies in this book can be applied to excerpts from popular, canonical, and semi-historical literature. Additionally, a number of the tools and templates in the book are available as free eResources from our website (http://www.routledge.com/9781138950658), so you can start using them immediately in your classroom. |
| example of a text analysis: How to Analyse Texts Ronald Carter, Angela Goddard, 2015-08-20 How to Analyse Texts is the essential introductory textbook and toolkit for language analysis. This book shows the reader how to undertake detailed, language-focussed, contextually sensitive analyses of a wide range of texts – spoken, written and multimodal. The book constitutes a flexible resource which can be used in different ways across a range of courses and at different levels. This textbook includes: three parts covering research and study skills, language structure and use, and how texts operate in sociocultural contexts a wide range of international real-life texts, including items from South China Morning Post, art’otel Berlin and Metro Sweden, which cover digital and print media, advertising, recipes and much more objectives and skill review for each section, activities, commentaries, suggestions for independent assignments, and an analysis checklist for students to follow a combined glossary and index and a comprehensive further reading section a companion website at www.routledge.com/cw/goddard with further links and exercises for students. Written by two experienced teachers of English Language, How to Analyse Texts is key reading for all students of English language and linguistics. |
| example of a text analysis: Text Analytics with Python Dipanjan Sarkar, 2016-11-30 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 |
| example of a text analysis: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| example of a text analysis: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| example of a text analysis: Computer-Assisted Text Analysis Roel Popping, 2000-02-28 Providing an up-to-date picture of the main methods for the quantitative analysis of text, this book begins by overviewing the background and the conceptual foundations of the field. The author then covers the traditional thematic approaches of text analysis, followed by an explanation of newer developments in semantic and network text analysis methodologies. Finally, he examines the relationship between content analysis and other kinds of text analysis - from qualitative research, linguistic analysis and information retrieval. Computer-assisted Text Analysis focuses on the methodological and practical issues of coding and handling data, including sampling, reliability and validity issues, and includes a useful appendix of computer programs for text analysis. |
| example of a text analysis: Handbook of Market Research Christian Homburg, Martin Klarmann, Arnd Vomberg, 2021-12-03 In this handbook, internationally renowned scholars outline the current state-of-the-art of quantitative and qualitative market research. They discuss focal approaches to market research and guide students and practitioners in their real-life applications. Aspects covered include topics on data-related issues, methods, and applications. Data-related topics comprise chapters on experimental design, survey research methods, international market research, panel data fusion, and endogeneity. Method-oriented chapters look at a wide variety of data analysis methods relevant for market research, including chapters on regression, structural equation modeling (SEM), conjoint analysis, and text analysis. Application chapters focus on specific topics relevant for market research such as customer satisfaction, customer retention modeling, return on marketing, and return on price promotions. Each chapter is written by an expert in the field. The presentation of the material seeks to improve the intuitive and technical understanding of the methods covered. |
| example of a text analysis: The Little Prince Antoine de Saint−Exupery, 2021-08-31 The Little Prince and nbsp;(French: and nbsp;Le Petit Prince) is a and nbsp;novella and nbsp;by French aristocrat, writer, and aviator and nbsp;Antoine de Saint-Exupéry. It was first published in English and French in the US by and nbsp;Reynal and amp; Hitchcock and nbsp;in April 1943, and posthumously in France following the and nbsp;liberation of France and nbsp;as Saint-Exupéry's works had been banned by the and nbsp;Vichy Regime. The story follows a young prince who visits various planets in space, including Earth, and addresses themes of loneliness, friendship, love, and loss. Despite its style as a children's book, and nbsp;The Little Prince and nbsp;makes observations about life, adults and human nature. The Little Prince and nbsp;became Saint-Exupéry's most successful work, selling an estimated 140 million copies worldwide, which makes it one of the and nbsp;best-selling and nbsp;and and nbsp;most translated books and nbsp;ever published. and nbsp;It has been translated into 301 languages and dialects. and nbsp;The Little Prince and nbsp;has been adapted to numerous art forms and media, including audio recordings, radio plays, live stage, film, television, ballet, and opera. |
| example of a text analysis: Analysing Discourse Norman Fairclough, 2003 The book is an essential resource seeking to analyze real texts and discourse.--BOOK JACKET. |
| example of a text analysis: Computational Conflict Research Emanuel Deutschmann, Jan Lorenz, Luis G. Nardin, Davide Natalini, Adalbert F. X. Wilhelm, 2019-11-09 This open access book brings together a set of original studies that use cutting-edge computational methods to investigate conflict at various geographic scales and degrees of intensity and violence. Methodologically, this book covers a variety of computational approaches from text mining and machine learning to agent-based modelling and social network analysis. Empirical cases range from migration policy framing in North America and street protests in Iran to violence against civilians in Congo and food riots world-wide. Supplementary materials in the book include a comprehensive list of the datasets on conflict and dissent, as well as resources to online repositories where the annotated code and data of individual chapters can be found and where (agent-based) models can be re-produced and altered. These materials are a valuable resource for those wishing to retrace and learn from the analyses described in this volume and adapt and apply them to their own research interests. By bringing together novel research through an international team of scholars from a range of disciplines, Computational Conflict Research pioneers and maps this emerging field. The book will appeal to students, scholars, and anyone interested in the prospects of using computational social sciences to advance our understanding of conflict dynamics. |
| example of a text analysis: House of Leaves Mark Z. Danielewski, 2000-03-07 “A novelistic mosaic that simultaneously reads like a thriller and like a strange, dreamlike excursion into the subconscious.” —The New York Times Years ago, when House of Leaves was first being passed around, it was nothing more than a badly bundled heap of paper, parts of which would occasionally surface on the Internet. No one could have anticipated the small but devoted following this terrifying story would soon command. Starting with an odd assortment of marginalized youth -- musicians, tattoo artists, programmers, strippers, environmentalists, and adrenaline junkies -- the book eventually made its way into the hands of older generations, who not only found themselves in those strangely arranged pages but also discovered a way back into the lives of their estranged children. Now this astonishing novel is made available in book form, complete with the original colored words, vertical footnotes, and second and third appendices. The story remains unchanged, focusing on a young family that moves into a small home on Ash Tree Lane where they discover something is terribly wrong: their house is bigger on the inside than it is on the outside. Of course, neither Pulitzer Prize-winning photojournalist Will Navidson nor his companion Karen Green was prepared to face the consequences of that impossibility, until the day their two little children wandered off and their voices eerily began to return another story -- of creature darkness, of an ever-growing abyss behind a closet door, and of that unholy growl which soon enough would tear through their walls and consume all their dreams. |
| example of a text analysis: Script Analysis for Actors, Directors, and Designers James Michael Thomas, 2009 Script Analysis specifically for Actors, Directors, and Designers; the only book on this subject that covers the growing area of unconventional plays. |
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion of Example.
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. Learn more.
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern or model, as of something to be imitated or …
Example - definition of example by The Free Dictionary
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be imitated or avoided: to set a good example. 3. an …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE - Meaning & Translations | Collins English Dictionary
An example of something is a particular situation, object, or person which shows that what is being claimed is true. 2. An example of a particular class of objects or styles is something that …
example noun - Definition, pictures, pronunciation and usage …
used to emphasize something that explains or supports what you are saying; used to give an example of what you are saying. There is a similar word in many languages, for example in …
Example - Definition, Meaning & Synonyms - Vocabulary.com
An example is a particular instance of something that is representative of a group, or an illustration of something that's been generally described. Example comes from the Latin word …
example - definition and meaning - Wordnik
noun Something that serves as a pattern of behaviour to be imitated (a good example) or not to be imitated (a bad example). noun A person punished as a warning to others. noun A parallel …
EXAMPLE Synonyms: 20 Similar Words - Merriam-Webster
Some common synonyms of example are case, illustration, instance, sample, and specimen. While all these words mean "something that exhibits distinguishing characteristics in its …
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion of Example.
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. Learn more.
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern or model, as of something to be imitated or …
Example - definition of example by The Free Dictionary
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be imitated or avoided: to set a good example. 3. an …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE - Meaning & Translations | Collins English Dictionary
An example of something is a particular situation, object, or person which shows that what is being claimed is true. 2. An example of a particular class of objects or styles is something that …
example noun - Definition, pictures, pronunciation and usage …
used to emphasize something that explains or supports what you are saying; used to give an example of what you are saying. There is a similar word in many languages, for example in …
Example - Definition, Meaning & Synonyms - Vocabulary.com
An example is a particular instance of something that is representative of a group, or an illustration of something that's been generally described. Example comes from the Latin word …
example - definition and meaning - Wordnik
noun Something that serves as a pattern of behaviour to be imitated (a good example) or not to be imitated (a bad example). noun A person punished as a warning to others. noun A parallel …
EXAMPLE Synonyms: 20 Similar Words - Merriam-Webster
Some common synonyms of example are case, illustration, instance, sample, and specimen. While all these words mean "something that exhibits distinguishing characteristics in its …
