November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Summary
We started this chapter with a discussion about the KDE and its usefulness in understanding the underlying distribution of data. We proceeded by explaining how to extract tweets from Twitter for a given search string in R. Then, we proceeded to explain the sentiment ming, dictionary, and machine learning approaches. Using a dictionary approach, we calculated the sentiment scores for the tweets. We further explained text pre-processing routines required to prepare the text data. We covered weighting schemes for creating document term matrixes. We discussed the classic tfidf and the new Delta TFIDF schemes. We created our training set using the Delta TFIDF scheme. Using this training set, we finally built a Naive Bayes KDE classifier ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.