TFIDF tibble vs dtm

TFIDF can be used for document similarity, text classification, and tasks. Consider the tibble, left_right_tfidf, and the document-term matrix, left_right_matrix. Both have been loaded into the console.

Which of the following statements is true?

A: The tibble contains one row per document and a column for each word used in all of the text.
B: The tibble contains the word counts, tf, idf, and tfidf weights for each word in each document.
C: The tibble and the matrix have the same number of rows.
D: The columns of the document-term matrix can be used in classification models.

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Introduction to Natural Language Processing in R

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Introduction to Natural Language Processing in R

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Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.

Exercise 1: Regular expression basics Exercise 2: Practicing syntax with grep Exercise 3: Exploring regular expression functions.Exercise 4: Tokenization Exercise 5: tidytext functions Exercise 6: Tokenization: sentences Exercise 7: Text cleaning basics Exercise 8: Text preprocessing: remove stop words Exercise 9: Text preprocessing: Stemming

In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other. You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and 4.

Exercise 1: Understanding an R corpus Exercise 2: Explore an R corpus Exercise 3: Creating a tibble from a corpus Exercise 4: Creating a corpus Exercise 5: The bag-of-words representation Exercise 6: Practice BoW Exercise 7: BoW Example Exercise 8: Sparse matrices Exercise 9: The TFIDF Exercise 10: Manual calculations Exercise 11: TFIDF Practice Exercise 12: Cosine Similarity Exercise 13: An example of failing at text analysis Exercise 14: Cosine similarity example

Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.

Exercise 1: Preparing text for modeling Exercise 2: Data preparation Exercise 3: Removing sparse terms Exercise 4: Classification modeling Exercise 5: Classification modeling example Exercise 6: Confusion matrices Exercise 7: TFIDF tibble vs dtm

Current Exercise

Exercise 8: Introduction to topic modeling Exercise 9: LDA practice Exercise 10: Assigning topics to documents Exercise 11: LDA in practice Exercise 12: Testing perplexity Exercise 13: Reviewing LDA results

In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course.

Exercise 1: Sentiment analysis Exercise 2: tidytext lexicons Exercise 3: Sentiment scores Exercise 4: Sentiment and emotion Exercise 5: Word embeddings Exercise 6: h2o practice Exercise 7: word2vec Exercise 8: Additional NLP analysis Exercise 9: Reviewing methods #1 Exercise 10: Review methods #2 Exercise 11: Conclusion