Weka and LibSVM usage

Sample.java

DatabaseLoader databaseLoader = new DatabaseLoader();

String q = "SELECT news, type FROM table_name

databaseLoader.setUser("root"); // username
databaseLoader.setPassword(""); // password
databaseLoader.setQuery(q);

// load the data
Instances dataUnfiltered0 = databaseLoader.getDataSet();

// coverting the 'type' column into nominal values

StringToNominal filter0 = new StringToNominal();
String[] options = weka.core.Utils.splitOptions("-R last"); //apply only to last column (type)
filter0.setOptions(options);
filter0.setInputFormat(dataUnfiltered0);
Instances dataFiltered0 = Filter.useFilter(dataUnfiltered0, filter0);
dataFiltered0.setClassIndex(dataFiltered0.numAttributes() - 1);

// converting textual data to numerical data

//create new filter for vector transformation
StringToWordVector filter = new StringToWordVector();
filter.setLowerCaseTokens(true);
filter.setOutputWordCounts(true);
// you can set some more options to filter here

filter.setInputFormat(dataFiltered0);
Instances dataFiltered = Filter.useFilter(dataFiltered0, filter);
System.out.println("\n\nFiltered data:\n\n" + dataFiltered);

//initialize the model

LibSVM svm = new LibSVM();
// you can set more options to svm here

// evaluating the model

Evaluation evaluation = new Evaluation(dataFiltered);
evaluation.crossValidateModel(svm, dataFiltered, 2, new Random(1));
System.out.println(evaluation.toSummaryString());
System.out.println(evaluation.weightedAreaUnderROC());