Join Barton Poulson for an in-depth discussion in this video Text mining in R, part of Data Science Foundations: Data Mining.
- [Narrator] The first thing we need to do…is load some packages that we're going to be using.…I use pacman simply to manage packages,…tm is a text mining and that will give us…most of our functionality.…SnowballC adds some additional text analysis,…and dplyr is for manipulating data…and for arranging the code using pipes,…where the output of one command…feeds directly into the input of another one.…We'll begin by importing our data.…I have everything in the same directory,…so I don't need to give a specific file path,…and also, these two books I've taken…from project Gutenberg, but I've already stripped off…the metadata at the beginning and the end of the documents,…so all that's left is the novel itself,…without the boilerplate text.…
We'll start by getting Jane Eyre…and using the readLines commands,…we'll read that text document into bookJE, for Jane Eyre.…All right, that is now readed in,…and then we'll do the same for Wuthering Heights,…except we'll go into bookWH for Wuthering Heights.…Now, when it reads the lines, it's reading them…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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