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Book Synopsis Computational Approaches to Text Understanding by : Steen Jansen
Download or read book Computational Approaches to Text Understanding written by Steen Jansen and published by Museum Tusculanum Press. This book was released on 1992 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of current issues in Denmark.
Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field. Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analytics—and the underlying principles at work—and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.
Book Synopsis Text as Data by : Barry DeVille
Download or read book Text as Data written by Barry DeVille and published by John Wiley & Sons. This book was released on 2021-09-29 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field. Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analytics—and the underlying principles at work—and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.
This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.
Book Synopsis Clinical Text Mining by : Hercules Dalianis
Download or read book Clinical Text Mining written by Hercules Dalianis and published by Springer. This book was released on 2018-05-14 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.
Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly, Volume 170 in the Progress in Molecular Biology and Translational Science series, provides the most topical, informative and exciting monographs available on a wide variety of research topics. The series includes in-depth knowledge on the molecular biological aspects of organismal physiology, with this release including chapters on Pairwise-Additive and Polarizable Atomistic Force Fields for Molecular Dynamics Simulations of Proteins, Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers, Enhanced sampling and free energy methods, and much more. Includes comprehensive coverage on molecular biology Presents ample use of tables, diagrams, schemata and color figures to enhance the reader's ability to rapidly grasp the information provided Contains contributions from renowned experts in the field
Book Synopsis Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly by :
Download or read book Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly written by and published by Academic Press. This book was released on 2020-03-05 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly, Volume 170 in the Progress in Molecular Biology and Translational Science series, provides the most topical, informative and exciting monographs available on a wide variety of research topics. The series includes in-depth knowledge on the molecular biological aspects of organismal physiology, with this release including chapters on Pairwise-Additive and Polarizable Atomistic Force Fields for Molecular Dynamics Simulations of Proteins, Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers, Enhanced sampling and free energy methods, and much more. Includes comprehensive coverage on molecular biology Presents ample use of tables, diagrams, schemata and color figures to enhance the reader's ability to rapidly grasp the information provided Contains contributions from renowned experts in the field
In the past few decades the use of increasingly large text corpora has grown rapidly in language and linguistics research. This was enabled by remarkable strides in natural language processing (NLP) technology, technology that enables computers to automatically and efficiently process, annotate and analyze large amounts of spoken and written text in linguistically and/or pragmatically meaningful ways. It has become more desirable than ever before for language and linguistics researchers who use corpora in their research to gain an adequate understanding of the relevant NLP technology to take full advantage of its capabilities. This volume provides language and linguistics researchers with an accessible introduction to the state-of-the-art NLP technology that facilitates automatic annotation and analysis of large text corpora at both shallow and deep linguistic levels. The book covers a wide range of computational tools for lexical, syntactic, semantic, pragmatic and discourse analysis, together with detailed instructions on how to obtain, install and use each tool in different operating systems and platforms. The book illustrates how NLP technology has been applied in recent corpus-based language studies and suggests effective ways to better integrate such technology in future corpus linguistics research. This book provides language and linguistics researchers with a valuable reference for corpus annotation and analysis.
Book Synopsis Computational Methods for Corpus Annotation and Analysis by : Xiaofei Lu
Download or read book Computational Methods for Corpus Annotation and Analysis written by Xiaofei Lu and published by Springer. This book was released on 2014-07-08 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past few decades the use of increasingly large text corpora has grown rapidly in language and linguistics research. This was enabled by remarkable strides in natural language processing (NLP) technology, technology that enables computers to automatically and efficiently process, annotate and analyze large amounts of spoken and written text in linguistically and/or pragmatically meaningful ways. It has become more desirable than ever before for language and linguistics researchers who use corpora in their research to gain an adequate understanding of the relevant NLP technology to take full advantage of its capabilities. This volume provides language and linguistics researchers with an accessible introduction to the state-of-the-art NLP technology that facilitates automatic annotation and analysis of large text corpora at both shallow and deep linguistic levels. The book covers a wide range of computational tools for lexical, syntactic, semantic, pragmatic and discourse analysis, together with detailed instructions on how to obtain, install and use each tool in different operating systems and platforms. The book illustrates how NLP technology has been applied in recent corpus-based language studies and suggests effective ways to better integrate such technology in future corpus linguistics research. This book provides language and linguistics researchers with a valuable reference for corpus annotation and analysis.
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.
Book Synopsis A Computational Approach to Statistical Learning by : Taylor Arnold
Download or read book A Computational Approach to Statistical Learning written by Taylor Arnold and published by CRC Press. This book was released on 2019-01-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.
Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned knowledge and expertise of traditional historical linguistics with cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge. The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems — e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives.
Book Synopsis Computational approaches to semantic change by : Nina Tahmasebi
Download or read book Computational approaches to semantic change written by Nina Tahmasebi and published by Language Science Press. This book was released on 2021-08-30 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned knowledge and expertise of traditional historical linguistics with cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge. The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems — e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives.
The book will appeal to scholars and advanced students of morphology, syntax, computational linguistics and natural language processing (NLP). It provides a critical and practical guide to computational techniques for handling morphological and syntactic phenomena, showing how these techniques have been used and modified in practice. The authors discuss the nature and uses of syntactic parsers and examine the problems and opportunities of parsing algorithms for finite-state, context-free and various context-sensitive grammars. They relate approaches for describing syntax and morphology to formal mechanisms and algorithms, and present well-motivated approaches for augmenting grammars with weights or probabilities.
Book Synopsis Computational Approaches to Morphology and Syntax by : Brian Roark
Download or read book Computational Approaches to Morphology and Syntax written by Brian Roark and published by OUP Oxford. This book was released on 2007-08-09 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book will appeal to scholars and advanced students of morphology, syntax, computational linguistics and natural language processing (NLP). It provides a critical and practical guide to computational techniques for handling morphological and syntactic phenomena, showing how these techniques have been used and modified in practice. The authors discuss the nature and uses of syntactic parsers and examine the problems and opportunities of parsing algorithms for finite-state, context-free and various context-sensitive grammars. They relate approaches for describing syntax and morphology to formal mechanisms and algorithms, and present well-motivated approaches for augmenting grammars with weights or probabilities.
This book tries to answer the question posed by Minsky at the beginning of The Society of Mind: "to explain the mind, we have to show how minds are built from mindless stuff, from parts that are much smaller and simpler than anything we'd considered smart." The author believes that cognition should not be rooted in innate rules and primitives, but rather grounded in human memory. More specifically, he suggests viewing linguistic comprehension as a time-constrained process -- a race for building an interpretation in short term memory. After reviewing existing psychological and computational approaches to text understanding and concluding that they generally rely on self-validating primitives, the author abandons this objectivist and normative approach to meaning and develops a set of requirements for a grounded cognitive architecture. He then goes on to explain how this architecture must avoid all epistemological commitments, be tractable both with respect to space and time, and, most importantly, account for the diachronic and non-deterministic nature of comprehension. In other words, a text may or may not lead to an interpretation for a specific reader, and may be associated with several interpretations over time by one reader. Throughout the remainder of the book, the author demonstrates that rules for all major facets of comprehension -- syntax, reference resolution, quantification, lexical and structural disambiguation, inference and subject matter -- can be expressed in terms of the simple mechanistic computing elements of a massively parallel network modeling memory. These elements, called knowledge units, work in a limited amount of time and have the ability not only to recognize but also to build the structures that make up an interpretation. Designed as a main text for graduate courses, this volume is essential to the fields of cognitive science, artificial intelligence, memory modeling, text understanding, computational linguistics and natural language understanding. Other areas of application are schema-matching, hermeneutics, local connectionism, and text linguistics. With its extensive bibliography, the book is also valuable as supplemental reading for introductory undergraduate courses in cognitive science and computational linguistics.
Book Synopsis Time-constrained Memory by : Jean-Pierre Corriveau
Download or read book Time-constrained Memory written by Jean-Pierre Corriveau and published by Psychology Press. This book was released on 2014-02-04 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tries to answer the question posed by Minsky at the beginning of The Society of Mind: "to explain the mind, we have to show how minds are built from mindless stuff, from parts that are much smaller and simpler than anything we'd considered smart." The author believes that cognition should not be rooted in innate rules and primitives, but rather grounded in human memory. More specifically, he suggests viewing linguistic comprehension as a time-constrained process -- a race for building an interpretation in short term memory. After reviewing existing psychological and computational approaches to text understanding and concluding that they generally rely on self-validating primitives, the author abandons this objectivist and normative approach to meaning and develops a set of requirements for a grounded cognitive architecture. He then goes on to explain how this architecture must avoid all epistemological commitments, be tractable both with respect to space and time, and, most importantly, account for the diachronic and non-deterministic nature of comprehension. In other words, a text may or may not lead to an interpretation for a specific reader, and may be associated with several interpretations over time by one reader. Throughout the remainder of the book, the author demonstrates that rules for all major facets of comprehension -- syntax, reference resolution, quantification, lexical and structural disambiguation, inference and subject matter -- can be expressed in terms of the simple mechanistic computing elements of a massively parallel network modeling memory. These elements, called knowledge units, work in a limited amount of time and have the ability not only to recognize but also to build the structures that make up an interpretation. Designed as a main text for graduate courses, this volume is essential to the fields of cognitive science, artificial intelligence, memory modeling, text understanding, computational linguistics and natural language understanding. Other areas of application are schema-matching, hermeneutics, local connectionism, and text linguistics. With its extensive bibliography, the book is also valuable as supplemental reading for introductory undergraduate courses in cognitive science and computational linguistics.
Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
Book Synopsis Computational Analysis of Communication by : Wouter van Atteveldt
Download or read book Computational Analysis of Communication written by Wouter van Atteveldt and published by John Wiley & Sons. This book was released on 2022-03-02 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
