code: package zemberek.classification;
  
  import java.io.IOException;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.nio.file.Paths;
  import java.util.ArrayList;
  import java.util.List;
  import zemberek.apps.fasttext.EvaluateClassifier;
  import zemberek.apps.fasttext.TrainClassifier;
  import zemberek.core.collections.Histogram;
  import zemberek.core.logging.Log;
  import zemberek.core.text.TextIO;
  import zemberek.examples.classification.ClassificationExampleBase;
  import zemberek.morphology.TurkishMorphology;
  import zemberek.morphology.lexicon.RootLexicon;
  import zemberek.normalization.TurkishSentenceNormalizer;
  
  public class SentimentClassifier extends ClassificationExampleBase {
  
    static Path root = Paths.get("/home/aaa/data/sentiment");
    static Path t1out = root.resolve("t1");
    static Path trainRaw = t1out.resolve("train-raw");
    static Path testRaw = t1out.resolve("test-raw");
  
  
    public static void main(String[] args) throws IOException {
  
      SentimentClassifier experiment = new SentimentClassifier();
      morphology = TurkishMorphology.builder()
          .setLexicon(RootLexicon.getDefault())
          .build();
  
      Path dataRoot = Paths.get("/home/aaa/zemberek-data");
      normalizer = new TurkishSentenceNormalizer(
          morphology,
          dataRoot.resolve("normalization"),
          dataRoot.resolve("lm/lm.2gram.slm"));
  
      experiment.generateData();
  
      List<String> trainRawLines = TextIO.loadLines(trainRaw);
      List<String> testRawLines = TextIO.loadLines(testRaw);
  
      Log.info("Train data:");
      experiment.dataInfo(trainRawLines);
      Log.info("Test data:");
      experiment.dataInfo(testRawLines);
  
      Path tokenizedTrain = t1out.resolve("train-tokenized");
      Path tokenizedTest = t1out.resolve("test-tokenized");
  
      experiment.generateSetTokenized(trainRawLines, tokenizedTrain);
      experiment.generateSetTokenized(testRawLines, tokenizedTest);
  
      experiment.evaluate(t1out, tokenizedTrain, tokenizedTest, "tokenized");
  
      Path lemmaTrain = t1out.resolve("train-lemma");
      Path lemmaTest = t1out.resolve("test-lemma");
  
      experiment.generateSetWithLemmas(trainRawLines, lemmaTrain);
      experiment.generateSetWithLemmas(testRawLines, lemmaTest);
  
      experiment.evaluate(t1out, lemmaTrain, lemmaTest, "lemma");
  
      Path splitTrain = t1out.resolve("train-split");
      Path splitTest = t1out.resolve("test-split");
  
      experiment.generateSetWithSplit(trainRawLines, splitTrain);
      experiment.generateSetWithSplit(testRawLines, splitTest);
  
      experiment.evaluate(t1out, splitTrain, splitTest, "split");
  
    }
  
    void generateData() throws IOException {
      Path t1Train = root.resolve("raw/t1/train.csv");
      Path t1Test = root.resolve("raw/t1/test.csv");
      Files.createDirectories(t1out);
      Files.write(trainRaw, addLabels(t1Train));
      Files.write(testRaw, addLabels(t1Test));
    }
  
    static List<String> addLabels(Path input) throws IOException {
      List<String> lines = TextIO.loadLines(input);
      List<String> result = new ArrayList<>();
      for (String line : lines) {
        int i = line.indexOf('\t');
        if (i == -1) {
          continue;
        }
        String content = line.substring(0, i).trim();
        normalizer.setAlwaysApplyDeasciifier(true);
        content = normalizer.normalize(content);
        String label = "__label__" + line.substring(i).trim();
        result.add(label + " " + content);
      }
      return result;
    }
  
    private void evaluate(Path root, Path train, Path test, String name) {
  
      //Create model if it does not exist.
      Path modelPath = root.resolve(name + ".model");
      if (!modelPath.toFile().exists()) {
        new TrainClassifier().execute(
            "-i", train.toString(),
            "-o", modelPath.toString(),
            "--learningRate", "0.1",
            "--epochCount", "70",
            "--dimension", "100",
            "--wordNGrams", "3"/*,
            "--applyQuantization",
            "--cutOff", "25000"*/
        );
      }
      Log.info("Testing...");
      test(test, root.resolve(name + ".predictions"), modelPath);
      // test quantized models.
  /*
      Log.info("Testing with quantized model...");
      test(test, root.resolve(name + ".predictions.q"), root.resolve(name + ".model.q"));
  */
    }
  
    private void test(Path testPath, Path predictionsPath, Path modelPath) {
      new EvaluateClassifier().execute(
          "-i", testPath.toString(),
          "-m", modelPath.toString(),
          "-o", predictionsPath.toString(),
          "-k", "1"
      );
    }
  
    void dataInfo(List<String> lines) {
      Log.info("Total lines = " + lines.size());
      Histogram<String> hist = new Histogram<>();
      lines.stream()
          .map(s -> s.substring(0, s.indexOf(' ')))
          .forEach(hist::add);
      Log.info("Categories :");
      for (String s : hist.getSortedList()) {
        Log.info(s + " " + hist.getCount(s));
      }
    }
  
  }
  
  