David Smith has a post summarizing sentiment analysis with Microsoft R Server:

Tsuyoshi Matsuzaki demonstrates the process in a post at the MSDN Blog. The post explores the Multi-Domain Sentiment Dataset, a collection of product reviews from Amazon.com. The dataset includes reviews from 975,194 products on Amazon.com from a variety of domains, and for each product there is a text review and a star rating of 1, 2, 4, or 5. (There are no 3-star rated reviews in the data set.) Here’s one example, selected at random:

What a useful reference! I bought this book hoping to brush up on my French after a few years of absence, and found it to be indispensable. It’s great for quickly looking up grammatical rules and structures as well as vocabulary-building using the helpful vocabulary lists throughout the book. My personal favorite feature of this text is Part V, Idiomatic Usage. This section contains extensive lists of idioms, grouped by their root nouns or verbs. Memorizing one or two of these a day will do wonders for your confidence in French. This book is highly recommended either as a standalone text, or, preferably, as a supplement to a more traditional textbook. In either case, it will serve you well in your continuing education in the French language.

The review contains many positive terms (“useful”, “indespensable”, “highly recommended”), and in fact is associated with a 5-star rating for this book. The goal of the blog post was to find the terms most associated with positive (or negative) reviews. One way to do this is to use the featurizeText function in thje Microsoft ML package included with Microsoft R Client and Microsoft R Server. Among other things, this function can be used to extract ngrams (sequences of one, two, or more words) from arbitrary text. In this example, we extract all of the one and two-word sequences represented at least 500 times in the reviews. Then, to assess which have the most impact on ratings, we use their presence or absence as predictors in a linear model:

If you’re thinking about sentiment analysis, read the whole thing.

Rachael Tatman shows how to tokenize a set of text as the first step in a natural language processing experiment:

In this tutorial you’ll learn how to:

• Read text into R
• Select only certain lines
• Tokenize text using the tidytext package
• Calculate token frequency (how often each token shows up in the dataset)
• Write reusable functions to do all of the above and make your work reproducible

For this tutorial we’ll be using a corpus of transcribed speech from bilingual children speaking in English.  You can find more information on this dataset and download it here.

It’s a nice tutorial, especially because the data set is a bit of a mess.

Bidyut Ghosh shows how to perform a one-way ANOVA test in R:

From the above results, it is observed that the F-statistic value is 17.94 and it is highly significant as the corresponding p-value is much less than the level of significance (1% or 0.01). Thus, it is wise to reject the null hypothesis of equal mean value of mileage run across all the tyre brands. In other words, the average mileage of the four tyre brands are not equal.
Now you have to find out the pair of brands which differ. For this you may use the Tukey’s HSD test.

ANOVA is a fairly simple test, but it can be quite useful to know.

Anish Sing Walia walks us through a gradient boosting exercise using R:

An important thing to remember in boosting is that the base learner which is being boosted should not be a complex and complicated learner which has high variance for e.g a neural network with lots of nodes and high weight values.For such learners boosting will have inverse effects.

So I will explain Boosting with respect to decision trees in this tutorial because they can be regarded as weak learners most of the times.We will generate a gradient boosting model.

Click through for more details.  H/T R-Bloggers

Peter Laurinec walks us through an example of using regression trees to solve a problem with double-seasonal time series data in R:

Classification and regression tree (or decision tree) is broadly used machine learning method for modeling. They are favorite because of these factors:

• simple to understand (white box)
• from a tree we can extract interpretable results and make simple decisions
• they are helpful for exploratory analysis as binary structure of tree is simple to visualize
• very good prediction accuracy performance
• very fast
• they can be simply tuned by ensemble learning techniques

But! There is always some “but”, they poorly adapt when new unexpected situations (values) appears. In other words, they can not detect and adapt to change or concept drift well (absolutely not). This is due to the fact that tree creates during learning just simple rules based on training data. Simple decision tree does not compute any regression coefficients like linear regression, so trend modeling is not possible. You would ask now, so why we are talking about time series forecasting with regression tree together, right? I will explain how to deal with it in more detail further in this post.

This was a very interesting article.  Absolutely worth reading.  H/T R-Bloggers