WORD MINING EBOOK
From Words to Wisdom: An Introduction to Text Mining with KNIME - Kindle edition by Vincenzo Kindle Store; ›; Kindle eBooks; ›; Computers & Technology . Charlie: The Final Word Mine For The Taking tells the true story of South London gangster Charlie Richardson and his remarkable life and crimes in South Africa. A guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools.
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Let's use unnest_tokens() to make a tidy data frame of all the words in our tweets, and remove the common English stop words. There are certain conventions in. eBook format -- $ for the 5, word list, $ for 10, words, and $ for the full Frequency Dictionary of American English: word sketches, collocates, and thematic lists. native, dry noun •use, acre•, water, •mine, area. The free list contains the lemma and part of speech for the top 5, words in American English. If you want an eBook version of the 5, word list -- with collocates, genre , mine, p, , , influence, n, , highway.
There are two ways of doing this, with each having unique advantages and disadvantages.
To generate as many reviews as possible for your new ebook, you might want to use both methods. Mining Goodreads for reviews: Method 1 The first method is listing a giveaway. This allows you to run a competition to giveaway free copies of your books to Goodreads members. The only cost to you is the cost of sending one copy of your book to the winner.
If you have a large marketing budget, you can giveaway more copies. The important thing to remember is that only physical copies can be given away in this manner—no ebooks!
So if your title is being released as an ebook only, this option is obviously not for you. Another important characteristic of tweets is how many times they are favorited or retweeted. We accessed our own tweets via the Twitter API and downloaded about tweets for each person.
In both cases, that is about the last 18 months worth of Twitter activity. This corresponds to a period of increasing activity and increasing numbers of followers for both of us. The first summarise statement counts how many times each word was retweeted, for each tweet and person. In the second summarise statement, we can find the median retweets for each person and word, also count the number of times each word was used ever by each person and keep that in uses.
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Next, we can join this to the data frame of retweet totals. At the top of this sorted data frame, we see tweets from Julia and David about packages that they work on, like gganimate and tidytext. We see lots of word about R packages, including tidytext, a package about which you are reading right now!
We can follow a similar procedure to see which words led to more favorites. Are they different than the words that lead to more retweets? We have built the data frames we need. We see some minor differences between Figures 7. In general, the same words that lead to retweets lead to favorites.
You can't just add two p-values together. The hell I can't: But- Dev: Is all statistics this easy. This chapter was our first case study, a beginning-to-end analysis that demonstrates how to bring together the concepts and code we have been exploring in a cohesive way to understand a text data set. Comparing word frequencies allows us to see which words we tweeted more and less frequently, and the log odds ratio shows us which words are more likely to be tweeted from each of our accounts.
Advanced Mine Ventilation
We can use nest and map with the glm function to find which words we have tweeted at higher and lower rates as time has passed. Finally, we can find which words in our tweets led to higher numbers of retweets and favorites.
All of these are examples of approaches to measure how we use words in similar and different ways and how the characteristics of our tweets are changing or compare with each other. These are flexible approaches to text mining that can be applied to other types of text as well.
Henry, Lionel, and Hadley Wickham.
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Never worry about losing your spot, and see how much time is left at a glance.Discover the best deal on audiobooks when you subscribe. We can then filter to only keep words used at least some minimum number of times 30, in this case.
Text Mining with R
In the course of his travels throughout South Africa, Charlie even meets Winnie Mandela, herself already an informant for South African intelligence. This book is the culmination of three years extensive research, during which the author conducted numerous lengthy interviews with Charlie Richardson, delving deep into memories long since buried to unearth the true and remarkable story behind a fascinating part of British gangland history.
We have built the data frames we need. Getting started mining Goodreads for reviews The first step to using Goodreads to generate book reviews is to make sure your book is actually listed on the site.
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