Java를 사용하여 감정 분석을 기반으로 지능형 텍스트 분류 시스템을 작성하는 방법

// 加载训练数据
DataSource source = new DataSource("train.arff");
Instances train = source.getDataSet();
train.setClassIndex(train.numAttributes()-1);

// 构建模型
BayesNet classifier = new BayesNet();
classifier.buildClassifier(train);

// 保存模型
ObjectOutputStream oos = new ObjectOutputStream(
new FileOutputStream("model.bin"));
oos.writeObject(classifier);
oos.flush();
oos.close();

// 加载模型
ObjectInputStream ois = new ObjectInputStream(
new FileInputStream("model.bin"));
BayesNet classifier = (BayesNet) ois.readObject();

// 构建待分类的实例
Instance instance = new DenseInstance(2);
instance.setValue(0, "这个电影真是太好看了！");
instance.setValue(1, "正片太赞，恶评都是骗点击的！");

// 进行分类
double label = classifier.classifyInstance(instance);
System.out.println("分类标签：" + train.classAttribute().value((int)label));

mvn archetype:generate -DgroupId=com.example -DartifactId=myproject -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false

@RestController
public class SentimentAnalysisController {

  private BayesNet classifier;

  public SentimentAnalysisController() {
    // 加载模型
    try {
      ObjectInputStream ois = new ObjectInputStream(
        new FileInputStream("model.bin"));
      classifier = (BayesNet) ois.readObject();
      ois.close();
    } catch (IOException | ClassNotFoundException e) {
      e.printStackTrace();
    }
  }

  @PostMapping("/predict")
  public String predict(@RequestBody Map<String, String> reqBody) {
    String text = reqBody.get("text"); // 获取待分类的文本
    Instance instance = createInstance(text); // 构建待分类的实例
    double label = classifier.classifyInstance(instance); // 进行分类
    return train.classAttribute().value((int)label); // 返回分类结果
  }

  private Instance createInstance(String text) {
    Instance instance = new DenseInstance(1);
    instance.setValue(0, text);
    instance.setDataset(new Instances(createAttributes(), 1));
    return instance;
  }

  private Instances createAttributes() {
    FastVector attributes = new FastVector();
    attributes.addElement(new Attribute("text", (FastVector) null));
    attributes.addElement(new Attribute("class", createClasses()));
    Instances instances = new Instances("data", attributes, 0);
    instances.setClassIndex(1);
    return instances;
  }

  private FastVector createClasses() {
    FastVector classes = new FastVector();
    classes.addElement("positive");
    classes.addElement("negative");
    return classes;
  }

}

mvn package
java -jar target/myproject-1.0-SNAPSHOT.jar

curl --request POST 
  --url http://localhost:8080/predict 
  --header 'content-type: application/json' 
  --data '{"text": "这个电影真是太好看了！"}'