Data needs to be pre-processed before it can be used for building an n-grams database. Learn about the steps need to pre-process text data.
- [Instructor] We will again use … the Course Description files under the courses directory … for predictive text analytics … which we have used in the earlier chapters. … The code for this chapter … is available in the file code_05_XX Predictive Text.R. … First, we need to prepare the data … for building an N-grams database … to do predictive text. … For this, we first load the course descriptions … into a VCorpus. … Then we convert the text to lowercase … and remove all punctuations … using the tm_map function. … Let's execute this code now. … In order to extract N-grams from the corpus, … we use the Rweka package. … Please install the same if it is not already available … in your setup. … I'm going to install this now. … The package has been successfully installed. … Let's load the library. … We first create a function called BigramTokenizer … that will convert a string into its bigrams … using the weka_control function available … in the Rweka package. … We then tokenize the strings …
- Creating a word cloud
- Analyzing sentiment
- Extracting emotions from text
- Clustering similar entities based on text
- Using classification for supervised learning
- Recommending items to users based on text data analytics
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Word Cloud
2. Sentiment Analysis
5. Predictive Text
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