title: Naive Bayes with Apache Spark MLlib
body: <p>I'm using Naive Bayes with Apache Spark MLlib for Text classification follow tutorial: <a href="http://avulanov.blogspot.com/2014/08/text-classification-with-apache-spark.html" rel="nofollow">http://avulanov.blogspot.com/2014/08/text-classification-with-apache-spark.html</a></p>
  
  <pre><code> /* instantiate Spark context (not needed for running inside Spark shell */
  val sc = new SparkContext("local", "test")
  /* word to vector space converter, limit to 10000 words */
  val htf = new HashingTF(10000)
  /* load positive and negative sentences from the dataset */
  /* let 1 - positive class, 0 - negative class */
  /* tokenize sentences and transform them into vector space model */
  val positiveData = sc.textFile("/data/rt-polaritydata/rt-polarity.pos")
    .map { text =&gt; new LabeledPoint(1, htf.transform(text.split(" ")))}
  val negativeData = sc.textFile("/data/rt-polaritydata/rt-polarity.neg")
    .map { text =&gt; new LabeledPoint(0, htf.transform(text.split(" ")))}
  /* split the data 60% for training, 40% for testing */
  val posSplits = positiveData.randomSplit(Array(0.6, 0.4), seed = 11L)
  val negSplits = negativeData.randomSplit(Array(0.6, 0.4), seed = 11L)
  /* union train data with positive and negative sentences */
  val training = posSplits(0).union(negSplits(0))
  /* union test data with positive and negative sentences */
  val test = posSplits(1).union(negSplits(1))
  /* Multinomial Naive Bayesian classifier */
  val model = NaiveBayes.train(training)
  /* predict */
  val predictionAndLabels = test.map { point =&gt;
    val score = model.predict(point.features)
    (score, point.label)
  }
  /* metrics */
  val metrics = new MulticlassMetrics(predictionAndLabels)
  /* output F1-measure for all labels (0 and 1, negative and positive) */
  metrics.labels.foreach( l =&gt; println(metrics.fMeasure(l)))
  </code></pre>
  
  <p>But, after training data. What should I do if I want to know sentence "Have a nice day" is positive or negative?
  Thank you.</p>