Support vector machines are statistical-and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.

Text mining is the process of turning free text into numerical variables and then analyzing them with statistical techniques. We introduce the command ngram, which implements the most common approach to text mining, the “bag of words”. An n-gram is a contiguous sequence of words in a text. Broadly speaking, ngram creates hundreds or thousands of variables, each recording how often the corresponding n-gram occurs in a given text. This is more useful than it sounds. We illustrate ngram with the categorization of text answers from two open-ended questions.

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