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Title | Supervised Machine Learning for Text Analysis in |
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Supervised Machine Learning for Text Analysis in R Welcome to Supervised Machine Learning for Text Analysis in R Preface Outline Topics this book will not cover Who is this book for? Acknowledgments Colophon I Natural Language Features 1 Language and modeling 1.1 Linguistics for text analysis 1.2 A glimpse into one area: morphology 1.3 Different languages 1.4 Other ways text can vary 1.5 Summary 1.5.1 In this chapter, you learned: 2 Tokenization 2.1 What is a token? 2.2 Types of tokens 2.2.1 Character tokens 2.2.2 Word tokens 2.2.3 Tokenizing by n-grams 2.2.4 Lines, sentence, and paragraph tokens 2.3 Where does tokenization break down? 2.4 Building your own tokenizer 2.4.1 Tokenize to characters, only keeping letters 2.4.2 Allow for hyphenated words 2.4.3 Wrapping it in a function 2.5 Tokenization for non-Latin alphabets 2.6 Tokenization benchmark 2.7 Summary 2.7.1 In this chapter, you learned: 3 Stop words 3.1 Using premade stop word lists 3.1.1 Stop word removal in R 3.2 Creating |
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