title: Naive Bayes vs. SVM for classifying text data
body: <p>I'm working on a problem that involves classifying a large database of texts. The texts are very short (think 3-8 words each) and there are 10-12 categories into which I wish to sort them. For the features, I'm simply using the tf–idf frequency of each word. Thus, the number of features is roughly equal to the number of words that appear overall in the texts (I'm removing stop words and some others).</p>
  
  <p>In trying to come up with a model to use, I've had the following two ideas:</p>
  
  <ul>
  <li>Naive Bayes (likely the sklearn multinomial Naive Bayes implementation)</li>
  <li>Support vector machine (with stochastic gradient descent used in training, also an sklearn implementation)</li>
  </ul>
  
  <p>I have built both models, and am currently comparing the results.</p>
  
  <p>What are the theoretical pros and cons to each model? Why might one of these be better for this type of problem? I'm new to machine learning, so what I'd like to understand is <em>why</em> one might do better.</p>
  
  <p>Many thanks!</p>