Join Barton Poulson for an in-depth discussion in this video Text mining in RapidMiner, part of Data Science Foundations: Data Mining.
- [Narrator] When we come to RapidMiner…we have a blank process and what we have to do…is install a few extensions…that give little bit of added functionality to RapidMiner.…To do this, you come up to Extensions,…and click on Marketplace.…You need to be connected to the internet…for that to work, but once it's there,…there are two extensions…that we're going to download and install.…The first one goes by a generic name, Text Processing.…You can search for that.…
Takes a moment.…This is the one we want.…Now, I already have it installed.…If it weren't installed, what I would do,…I'll just show you for instance right here.…This one I don't have installed.…You click "Select for installation"…and then it says, "Install 1 package."…I'm going to deselect that one, because I don't need it,…but this is one that I'm going to select to install,…and then there's another one that's going to…actually do the Sentiment Analysis.…This is called AYLIEN, and it's spelled in an unusual way,…A-Y-L-I-E-N.…
When we search for that,…you would also have the option of clicking right here…
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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