Price: $39.99 – $21.99
(as of Dec 28,2024 01:19:46 UTC – Details)
From the brand
Explore further ‘R’ resources
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (August 1, 2017)
Language : English
Paperback : 191 pages
ISBN-10 : 1491981652
ISBN-13 : 978-1491981658
Item Weight : 9.6 ounces
Dimensions : 6.9 x 0.4 x 9.1 inches
Customers say
Customers find the book easy to use and providing a comprehensive overview of text mining techniques. They appreciate the clear explanations and helpful tips for beginners. The book also covers sentiment analysis and trends using data from books.
AI-generated from the text of customer reviews
Text mining is a powerful tool for analyzing and extracting information from unstructured text data. In this post, we will explore how to perform text mining with R using a tidy approach.
Tidy text mining involves using the principles of tidy data, where each observation is a row and each variable is a column. This approach makes it easier to manipulate and analyze text data in a structured and organized manner.
To get started with text mining in R, we can use the tidytext package, which provides functions and tools for text mining using the tidy data principles. We can also use other popular packages such as dplyr and ggplot2 for data manipulation and visualization.
Some common text mining tasks that we can perform with R include:
1. Tokenization: Breaking down text data into individual words or tokens.
2. Stopword removal: Removing common words that do not add meaningful information to the analysis.
3. Term frequency analysis: Counting the frequency of words in a text document.
4. Sentiment analysis: Analyzing the sentiment or tone of the text data.
5. Topic modeling: Identifying themes or topics in a collection of text documents.
By using a tidy approach to text mining in R, we can easily perform these tasks and gain valuable insights from our text data. With the right tools and techniques, we can uncover patterns, trends, and relationships in our text data that can help us make better decisions and drive business outcomes.
So if you’re interested in diving into text mining with R and want to take a tidy approach, be sure to check out the tidytext package and start exploring the possibilities of analyzing text data in a structured and organized way. Happy mining!
#Text #Mining #Tidy #Approach
Leave a Reply