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6 TOPIC MODELING
As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book. In this chapter, we’ll learn to work with LDA objects from the topicmodels package, particularly tidying such models so that they can be manipulated with ggplot2 and dplyr.We’ll also explore an example of clustering chapters from several books 5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
10 REFERENCES
Abelson, Hal. 2008. “Foreword.” In Essentials of Programming Languages, 3rd Edition, 3rd ed.The MIT Press. 9 CASE STUDY: ANALYZING USENET TEXT 9 Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boardsin 1993.
2 SENTIMENT ANALYSIS WITH TIDY DATA 2 Sentiment analysis with tidy data. In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. 3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 7 CASE STUDY: COMPARING TWITTER ARCHIVES 7 Case study: comparing Twitter archives. One type of text that gets plenty of attention is text shared online via Twitter. In fact, several of the sentiment lexicons used in this book (and commonly used in general) were designed for use with and validated on tweets.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) WELCOME TO TEXT MINING WITH R "Text Mining with R: A Tidy Approach" was written by Julia Silge and David Robinson. It was last built on 2021-06-06. 1 THE TIDY TEXT FORMAT 1.3 Tidying the works of Jane Austen. Let’s use the text of Jane Austen’s 6 completed, published novels from the janeaustenr package (Silge 2016), and transform them into a tidy format.The janeaustenr package provides these texts in a one-row-per-line format, where a line in this context is analogous to a literal printed line in aphysical book.
6 TOPIC MODELING
As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book. In this chapter, we’ll learn to work with LDA objects from the topicmodels package, particularly tidying such models so that they can be manipulated with ggplot2 and dplyr.We’ll also explore an example of clustering chapters from several books 5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
10 REFERENCES
Abelson, Hal. 2008. “Foreword.” In Essentials of Programming Languages, 3rd Edition, 3rd ed.The MIT Press. 9 CASE STUDY: ANALYZING USENET TEXT 9 Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boardsin 1993.
2 SENTIMENT ANALYSIS WITH TIDY DATA 2 Sentiment analysis with tidy data. In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. 3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 7 CASE STUDY: COMPARING TWITTER ARCHIVES 7 Case study: comparing Twitter archives. One type of text that gets plenty of attention is text shared online via Twitter. In fact, several of the sentiment lexicons used in this book (and commonly used in general) were designed for use with and validated on tweets.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) PREFACE | TEXT MINING WITH R Using code examples. This book was written in RStudio using bookdown.The website is hosted via Netlify, and automatically built after every push by GitHub Actions.While we show the code behind the vast majority of the analyses, in the interest of space we sometimes choose not to show the code generating a particular visualization if we’ve already provided the code for several similar graphs. WELCOME TO TEXT MINING WITH R Welcome to Text Mining with R. This is the website for Text Mining with R! Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon. This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. 1 THE TIDY TEXT FORMAT 1 The tidy text format. 1. The tidy text format. Using tidy data principles is a powerful way to make handling data easier and more effective, and this is no less true when it comes to dealing with text. As described by Hadley Wickham (Wickham 2014), tidy data has a specific structure: Each variable is a column. Each observation is arow.
6 TOPIC MODELING
6. Topic modeling. In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds naturalgroups
5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA With data in a tidy format, sentiment analysis can be done as an inner join. This is another of the great successes of viewing text mining as a tidy data analysis task; much as removing stop words is an antijoin operation, performing sentiment analysis is an inner join operation. Let’s look at the words with a joy score from the NRC lexicon. 9 CASE STUDY: ANALYZING USENET TEXT 9. Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boards in 1993. The Usenet bulletin boards in this dataset include newsgroups for topics like politics, religion, cars, sports, and cryptography 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) WELCOME TO TEXT MINING WITH R Welcome to Text Mining with R. This is the website for Text Mining with R! Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon. This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. 1 THE TIDY TEXT FORMAT 1 The tidy text format. 1. The tidy text format. Using tidy data principles is a powerful way to make handling data easier and more effective, and this is no less true when it comes to dealing with text. As described by Hadley Wickham (Wickham 2014), tidy data has a specific structure: Each variable is a column. Each observation is arow.
6 TOPIC MODELING
6. Topic modeling. In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds naturalgroups
5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA With data in a tidy format, sentiment analysis can be done as an inner join. This is another of the great successes of viewing text mining as a tidy data analysis task; much as removing stop words is an antijoin operation, performing sentiment analysis is an inner join operation. Let’s look at the words with a joy score from the NRC lexicon. 9 CASE STUDY: ANALYZING USENET TEXT 9. Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boards in 1993. The Usenet bulletin boards in this dataset include newsgroups for topics like politics, religion, cars, sports, and cryptography 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html)TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html)10 REFERENCES
Abelson, Hal. 2008. “Foreword.” In Essentials of Programming Languages, 3rd Edition, 3rd ed.The MIT Press. PREFACE | TEXT MINING WITH R Using code examples. This book was written in RStudio using bookdown.The website is hosted via Netlify, and automatically built after every push by GitHub Actions.While we show the code behind the vast majority of the analyses, in the interest of space we sometimes choose not to show the code generating a particular visualization if we’ve already provided the code for several similar graphs. WELCOME TO TEXT MINING WITH R Welcome to Text Mining with R. This is the website for Text Mining with R! Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon. This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. 1 THE TIDY TEXT FORMAT 1 The tidy text format. 1. The tidy text format. Using tidy data principles is a powerful way to make handling data easier and more effective, and this is no less true when it comes to dealing with text. As described by Hadley Wickham (Wickham 2014), tidy data has a specific structure: Each variable is a column. Each observation is arow.
6 TOPIC MODELING
6. Topic modeling. In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds naturalgroups
5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA With data in a tidy format, sentiment analysis can be done as an inner join. This is another of the great successes of viewing text mining as a tidy data analysis task; much as removing stop words is an antijoin operation, performing sentiment analysis is an inner join operation. Let’s look at the words with a joy score from the NRC lexicon. 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
9 CASE STUDY: ANALYZING USENET TEXT 9. Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boards in 1993. The Usenet bulletin boards in this dataset include newsgroups for topics like politics, religion, cars, sports, and cryptography 3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) WELCOME TO TEXT MINING WITH R Welcome to Text Mining with R. This is the website for Text Mining with R! Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon. This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. 1 THE TIDY TEXT FORMAT 1 The tidy text format. 1. The tidy text format. Using tidy data principles is a powerful way to make handling data easier and more effective, and this is no less true when it comes to dealing with text. As described by Hadley Wickham (Wickham 2014), tidy data has a specific structure: Each variable is a column. Each observation is arow.
6 TOPIC MODELING
6. Topic modeling. In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds naturalgroups
5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA With data in a tidy format, sentiment analysis can be done as an inner join. This is another of the great successes of viewing text mining as a tidy data analysis task; much as removing stop words is an antijoin operation, performing sentiment analysis is an inner join operation. Let’s look at the words with a joy score from the NRC lexicon. 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
9 CASE STUDY: ANALYZING USENET TEXT 9. Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boards in 1993. The Usenet bulletin boards in this dataset include newsgroups for topics like politics, religion, cars, sports, and cryptography 3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) 3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this PREFACE | TEXT MINING WITH R Using code examples. This book was written in RStudio using bookdown.The website is hosted via Netlify, and automatically built after every push by GitHub Actions.While we show the code behind the vast majority of the analyses, in the interest of space we sometimes choose not to show the code generating a particular visualization if we’ve already provided the code for several similar graphs. WELCOME TO TEXT MINING WITH R "Text Mining with R: A Tidy Approach" was written by Julia Silge and David Robinson. It was last built on 2021-04-06. 1 THE TIDY TEXT FORMAT 1.3 Tidying the works of Jane Austen. Let’s use the text of Jane Austen’s 6 completed, published novels from the janeaustenr package (Silge 2016), and transform them into a tidy format.The janeaustenr package provides these texts in a one-row-per-line format, where a line in this context is analogous to a literal printed line in aphysical book.
6 TOPIC MODELING
As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book. In this chapter, we’ll learn to work with LDA objects from the topicmodels package, particularly tidying such models so that they can be manipulated with ggplot2 and dplyr.We’ll also explore an example of clustering chapters from several books 5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA 2 Sentiment analysis with tidy data. In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
9 CASE STUDY: ANALYZING USENET TEXT 9 Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boardsin 1993.
3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html) WELCOME TO TEXT MINING WITH R "Text Mining with R: A Tidy Approach" was written by Julia Silge and David Robinson. It was last built on 2021-04-06. 1 THE TIDY TEXT FORMAT 1.3 Tidying the works of Jane Austen. Let’s use the text of Jane Austen’s 6 completed, published novels from the janeaustenr package (Silge 2016), and transform them into a tidy format.The janeaustenr package provides these texts in a one-row-per-line format, where a line in this context is analogous to a literal printed line in aphysical book.
6 TOPIC MODELING
As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book. In this chapter, we’ll learn to work with LDA objects from the topicmodels package, particularly tidying such models so that they can be manipulated with ggplot2 and dplyr.We’ll also explore an example of clustering chapters from several books 5 CONVERTING TO AND FROM NON-TIDY FORMATS 5 Converting to and from non-tidy formats. In the previous chapters, we’ve been analyzing text arranged in the tidy text format: a table with one-token-per-document-per-row, such as is constructed by the unnest_tokens() function. This lets us use the popular suite of tidy tools such as dplyr, tidyr, and ggplot2 to explore and visualize textdata.
2 SENTIMENT ANALYSIS WITH TIDY DATA 2 Sentiment analysis with tidy data. In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. 8 CASE STUDY: MINING NASA METADATA 8 Case study: mining NASA metadata. There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself.We can use the metadata for these datasets to understand the connectionsbetween them.
9 CASE STUDY: ANALYZING USENET TEXT 9 Case study: analyzing usenet text. In our final chapter, we’ll use what we’ve learned in this book to perform a start-to-finish analysis of a set of 20,000 messages sent to 20 Usenet bulletin boardsin 1993.
3 ANALYZING WORD AND DOCUMENT FREQUENCY: TF-IDF 3.2 Zipf’s law. Distributions like those shown in Figure 3.1 are typical in language. In fact, those types of long-tailed distributions are so common in any given corpus of natural language (like a book, or a lot of text from a website, or spoken words) that the relationship between the frequency that a word is used and its rank has been the subject of study; a classic version of this 4 RELATIONSHIPS BETWEEN WORDS: N-GRAMS AND CORRELATIONS 4 Relationships between words: n-grams and correlations. So far we’ve considered words as individual units, and considered their relationships to sentiments or to documents. However, many interesting text analyses are based on the relationships between words, whether examining which words tend to follow others immediately, or that tend to co-occur within the same documents.TIDYTEXTMINING.COM
Redirecting to https://www.tidytextmining.com/intro.html)10 REFERENCES
Abelson, Hal. 2008. “Foreword.” In Essentials of Programming Languages, 3rd Edition, 3rd ed.The MIT Press. PREFACE | TEXT MINING WITH R Using code examples. This book was written in RStudio using bookdown.The website is hosted via Netlify, and automatically built after every push by GitHub Actions.While we show the code behind the vast majority of the analyses, in the interest of space we sometimes choose not to show the code generating a particular visualization if we’ve already provided the code for several similar graphs.Type to search
* Text Mining with R*
* __Welcome to Text Mining with R* __Preface
* __Outline
* __Topics this book does not cover* __About this book
* __Using code examples * __Acknowledgements * __1 The tidy text format * __1.1 Contrasting tidy text with other data structures * __1.2 The unnest_tokens function * __1.3 Tidying the works of Jane Austen * __1.4 The gutenbergr package * __1.5 Word frequencies* __1.6 Summary
* __2 Sentiment analysis with tidy data * __2.1 The sentiments dataset * __2.2 Sentiment analysis with inner join * __2.3 Comparing the three sentiment dictionaries * __2.4 Most common positive and negative words* __2.5 Wordclouds
* __2.6 Looking at units beyond just words* __2.7 Summary
* __3 Analyzing word and document frequency: tf-idf * __3.1 Term frequency in Jane Austen’s novels * __3.2 Zipf’s law * __3.3 The bind_tf_idf function * __3.4 A corpus of physics texts* __3.5 Summary
* __4 Relationships between words: n-grams and correlations * __4.1 Tokenizing by n-gram * __4.1.1 Counting and filtering n-grams * __4.1.2 Analyzing bigrams * __4.1.3 Using bigrams to provide context in sentiment analysis * __4.1.4 Visualizing a network of bigrams with ggraph * __4.1.5 Visualizing bigrams in other texts * __4.2 Counting and correlating pairs of words with the widyrpackage
* __4.2.1 Counting and correlating among sections * __4.2.2 Pairwise correlation* __4.3 Summary
* __5 Converting to and from non-tidy formats * __5.1 Tidying a document-term matrix * __5.1.1 Tidying DocumentTermMatrix objects * __5.1.2 Tidying dfm objects * __5.2 Casting tidy text data into a matrix * __5.3 Tidying corpus objects with metadata * __5.3.1 Example: mining financial articles* __5.4 Summary
* __6 Topic modeling * __6.1 Latent Dirichlet allocation * __6.1.1 Word-topic probabilities * __6.1.2 Document-topic probabilities * __6.2 Example: the great library heist * __6.2.1 LDA on chapters * __6.2.2 Per-document classification * __6.2.3 By word assignments: augment * __6.3 Alternative LDA implementations* __6.4 Summary
* __7 Case study: comparing Twitter archives * __7.1 Getting the data and distribution of tweets * __7.2 Word frequencies * __7.3 Comparing word usage * __7.4 Changes in word use * __7.5 Favorites and retweets* __7.6 Summary
* __8 Case study: mining NASA metadata * __8.1 How data is organized at NASA * __8.1.1 Wrangling and tidying the data * __8.1.2 Some initial simple exploration * __8.2 Word co-ocurrences and correlations * __8.2.1 Networks of Description and Title Words * __8.2.2 Networks of Keywords * __8.3 Calculating tf-idf for the description fields * __8.3.1 What is tf-idf for the description field words? * __8.3.2 Connecting description fields to keywords * __8.4 Topic modeling * __8.4.1 Casting to a document-term matrix * __8.4.2 Ready for topic modeling * __8.4.3 Interpreting the topic model * __8.4.4 Connecting topic modeling with keywords* __8.5 Summary
* __9 Case study: analyzing usenet text * __9.1 Pre-processing * __9.1.1 Pre-processing text * __9.2 Words in newsgroups * __9.2.1 Finding tf-idf within newsgroups * __9.2.2 Topic modeling * __9.3 Sentiment analysis * __9.3.1 Sentiment analysis by word * __9.3.2 Sentiment analysis by message * __9.3.3 N-gram analysis* __9.4 Summary
* __10 References
*
* Published with bookdown____
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__TEXT MINING WITH RTEXT MINING WITH R
_A TIDY APPROACH_
_Julia Silge and David Robinson__2019-09-02_
WELCOME TO TEXT MINING WITH R This is the website for _Text Mining with R_! Visit the GitHub repository for this site, find the book at
O’Reilly
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