Text mining techniques can be applied to various data sources (e.g., newspaper articles, emails, online discussion posts, etc.) to efficiently extract useful data for different research purposes. For example, health science researchers may be interested in investigating a frequency of a particular disease name mentioned in a large set of newspaper articles. Educational researchers, on the other side, may wish to extract and categorize students' opinions from discussion forum in a high enrollment course. R offers a comprehensive set of functionalities for text mining. In this workshop, you will learn how to implement basic methods for preprocessing textual data, metadata management, a creation of term-document matrices over the collection of textual documents, sentiment analysis, text tokenization, word relationship extraction and text visualization.
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