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.

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.

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.

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

"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"--

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.

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