Text Analysis is a broad term for a wide variety of specific analytic methods including parts-of-speech analysis, topic modeling, word frequency and concordances, distant reading, network analysis, data visualizations of unstructured data, and more. Web-based tools like Voyant provide easy access to a range of these techniques. This page is intended to provide some accessible resources for people who are interested in these methods. A more in-depth directory of tools that can be used for specific techniques is available through TAPoR.
Related techniques: Social Media Analysis, Qualitative Analysis
This technique is part of the Analysis activity.
Hermeneutica by
An introduction to text analysis using computer-assisted interpretive practices, accompanied by example essays that illustrate the use of these computational tools. The image of the scholar as a solitary thinker dates back at least to Descartes' Discourse on Method. But scholarly practices in the humanities are changing as older forms of communal inquiry are combined with modern research methods enabled by the Internet, accessible computing, data availability, and new media. Hermeneutica introduces text analysis using computer-assisted interpretive practices. It offers theoretical chapters about text analysis, presents a set of analytical tools (called Voyant) that instantiate the theory, and provides example essays that illustrate the use of these tools. Voyant allows users to integrate interpretation into texts by creating hermeneutica--small embeddable "toys" that can be woven into essays published online or into such online writing environments as blogs or wikis. The book's companion website, Hermeneuti.ca, offers the example essays with both text and embedded interactive panels. The panels show results and allow readers to experiment with the toys themselves. The use of these analytical tools results in a hybrid essay: an interpretive work embedded with hermeneutical toys that can be explored for technique. The hermeneutica draw on and develop such common interactive analytics as word clouds and complex data journalism interactives. Embedded in scholarly texts, they create a more engaging argument. Moving between tool and text becomes another thread in a dynamic dialogue.
Macroanalysis by
In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.
Natural Language Processing for Social Media by
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
Text Analysis with R for Students of Literature by
Text Analysis with R for Students of Literature 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 tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale "microanalysis" of single texts to large scale "macroanalysis" of text corpora, and each chapter 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.
Traces of the Old, Uses of the New by
Digital Humanities remains a contested, umbrella term covering many types of work in numerous disciplines, including literature, history, linguistics, classics, theater, performance studies, film, media studies, computer science, and information science. In Traces of the Old, Uses of the New: The Emergence of Digital Literary Studies, Amy Earhart stakes a claim for discipline-specific history of digital study as a necessary prelude to true progress in defining Digital Humanities as a shared set of interdisciplinary practices and interests. Traces of the Old, Uses of the New focuses on twenty-five years of developments, including digital editions, digital archives, e-texts, text mining, and visualization, to situate emergent products and processes in relation to historical trends of disciplinary interest in literary study. By reexamining the roil of theoretical debates and applied practices from the last generation of work in juxtaposition with applied digital work of the same period, Earhart also seeks to expose limitations in need of alternative methods--methods that might begin to deliver on the early (but thus far unfulfilled) promise that digitizing texts allows literature scholars to ask and answer questions in new and compelling ways. In mapping the history of digital literary scholarship, Earhart also seeks to chart viable paths to its future, and in doing this work in one discipline, this book aims to inspire similar work in others.
free | web-based | easy to learn
free for non-commercial use | Windows, Mac, Linux | medium difficulty
free if you download yourself; subscription needed for online use | web-based, but can be installed on Windows, Mac and Linux machines | challenging to install; easy to learn
free | web-based | easy to learn
free | Windows, Mac and Linux downloads | easy to learn
