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Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
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.
This book constitutes the proceedings of the 17th IFIP WG 8.5 International Conference on Electronic Government, EGOV 2018, held in Krems, Austria, in September 2018, in conjunction with the 10th International Conference on eParticipation, ePart 2018. The 22 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers are clustered under the following topical sections: General E-Government and Open Government; Open Data, Linked Data, and Semantic Web; Smart Governance (Government, Cities and Regions); and Artificial Intelligence, Data Analytics and Automated Decision-Making.
This book constitutes the refereed proceedings of the International Conference on Brain Informatics, BI 2018, held in Arlington, TX, USA, in December 2018. The 46 revised full papers were carefully reviewed and selected from 53 submissions. The papers are grouped thematically on cognitive and computational foundations of brain science, human information processing systems, brain big data analysis, curation and management, informatics paradigms for brain and mental health research, brain-machine intelligence and brain-inspired computing.
A hands on guide to web scraping and text mining for bothbeginners and experienced users of R Introduces fundamental concepts of the main architecture of theweb and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets(XPath and regular expressions). An extensive set of exercises are presented to guide thereader through each technique. Explores both supervised and unsupervised techniques as well asadvanced techniques such as data scraping and text management. Case studies are featured throughout along with examples foreach technique presented. R code and solutions to exercises featured in thebook are provided on a supporting website.

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