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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 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.

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