Text Mining, Scraping and Sentiment Analysis with R (Udemy.com)

It was overall OK, but 1) narration speed was too slow, 2) some of the actions shown couldn't be replicated without googling the problem and adding something that wasn't shown, 3) the coverage is rather limited, as there are many other tasks one might need to do, like extracting user information, separating tweets from retweets, comparing geographic distribution of sentiment, etc., etc.

I am still very new to R, so I do not understand everything that is taught completely. But I am able to follow along and get results. I have encountered some errors that were not addressed in the course, but so far I have been able to figure things out either via the Q&A or by looking around the internet for a few minutes.

A decent and quick introduction to text mining in R using Twitter data. The instructor does a very good job of outlining 2 different approaches to connecting to Twitter's developer API. Unfortunately the one area the course falls flat in is how to deal with the inevitable issues with unknown characters, etc. that you will have to deal with when pulling in tweets. If you are unable to deal with these characters, you really can't continue with the exercises in the course as all further formulas will throw errors and not run properly. I really wish the instructor spent more time discussing how to properly clean text data since it is such an important part of the job.

Great course, I really like the professor's deliberate, thorough teaching style, and the fact that he provides the R code for all the cool things he teaches you about web scraping, text analytics, and sentiment analysis. Plus he kinda sounds like Ahhnold Schwartzenegger, so that alone makes me want to listen all day.

This course provides a nice introduction to text mining and sentiment analysis in R. Although the course is rated as advance, I found it pretty easy and straightforward. It is not very time consuming either. The course is good and informative though and you'll get familiar with twitter data. It would be a nice addition to work with Facebook and LinkedIn data too.

This was pretty good. I knew very little about the scraping of Tweets so I found this valuable. I already knew a lot about Text Mining. I was very interested in sentiment analysis. The instructor's presentation of it was good but I would have liked to have learned other methods of sentiment analysis such as the ones in the qdap and syuzhet packages.

The content is comprehensive but some times a little basic. I expected far too much from an advanced R course. The course gives an interesting toolbox to deal with unstructured data which is very productive because is uncommon to analyze this kind of data. The quality of the course is good but could be improved in the written material.

This is a highly focused and well-designed class around Twitter and Sentiment analysis. The construction of the Breen sentiment scoring function is a nice takeaway. If you have never done Twitter or analysis of words, this is a good course in R to start with and build from.

Martin does a great job of teaching by example. While R is not the most complicated language out here, it has plenty of packages that are built for it. Martin has taken the topic of text mining, scraping, and sentiment analysis and shown how to accomplish these tasks through use of R. This is a growing area of study and this course will definitely show you what can be done. If you have any interest in this topic, the course will whet your appetite for more!

I really enjoyed this one especially among the previous courses. It is very practical and real life problem solving. I would appreciate to have some more real life problem and case solving tutorials.