More and more text data is being generated today. Learn why text processing is a unique challenge and how this course helps in this case.
- [Instructor] Text processing and analytics is one of the fastest growing areas in the field of machine learning. Why? Well, the truth is that more and more data that's getting generated today is text. The Internet contains a number of blogs, reviews, comments, notes, and other text-based facts. Social media generates data every day in the form of messages, tweets, hashtags, and references. Computer software generates log messages and audit trails. There is so much more, including emails and audio or video that gets transcribed into text. With so much free text data out there, businesses can capitalize through text analytics. They can then use these insights to drive strategic business actions. But, analyzing text possesses various unique challenges. Text data is several times as large as numeric data. Also, text data does not have a fixed structure or schema, and that makes understanding it difficult. In this course, I will show you some tools and techniques offered in R that can help you with these particular issues and aid in generating insights.
- 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