Have you always wanted to mine twitter data? Then this course is for you. This course presents example of text mining with R. Twitter text of @pycon and @udemy is used as the data to analyze. It starts by extracting text from Twitter. The extracted text is then transformed to a corpus and then a document-term matrix. After that, frequent words and associations are found from the matrix. A word cloud is used to present important words in documents.
There are three important packages used in the examples: twitteR, tm and wordcloud. Package twitteR provides access to Twitter data, tm provides functions for text mining, and wordcloud visualizes the result with a word cloud.
This course is meant for people who have basic knowledge of R and are interested in learning about text mining, in particular about how to mine data from Twitter. At the end of this course, you will be able to build term-document matrix and word clouds for any user on Twitter.
Nisha has been teaching since her grad school years as a Masters student in Computer Science where she worked as a teaching assistant for numerous courses in programming. Currently, she works in the Elearning industry and also helps students with programming problems. Nisha has worked as a software developer for various firms prior to teaching and understands how important it is to have a good grasp over programming fundamentals.
During her grad school, she has gained experience in teaching and how to effectively communicate a concept to someone new to programming. Nisha has worked with numerous students ranging from beginner to advanced and understands the needs of both kinds of audience.