How to write an intelligent text classification system based on sentiment analysis using Java

With the development of the Internet and social media, people continue to generate a variety of text data. How to extract useful information from massive text data has become an urgent problem that needs to be solved. Sentiment analysis, as a text classification technology, can help us automatically classify text and extract the emotional information of the text. This article will introduce how to use Java to write an intelligent text classification system based on sentiment analysis.

1. Obtain data

First, we need to obtain data suitable for sentiment analysis from the Internet. In general, a large amount of text data can be obtained through crawler technology. These text data need to be preprocessed, such as word segmentation, stop word removal, part-of-speech tagging, etc. This article does not involve crawlers and preprocessing technology. Readers can refer to other related tutorials to learn.

2. Training model

After obtaining the processed text data, we need to use this data to train a sentiment analysis model. We can choose to use deep learning techniques such as algorithms such as convolutional neural networks (CNN) or recurrent neural networks (RNN). Traditional machine learning techniques can also be used, such as Naive Bayes, Support Vector Machine (SVM) and other algorithms. In this article, we choose the Naive Bayes algorithm.

The Naive Bayes algorithm is a classification algorithm based on probability statistics. It assumes that all features are independent of each other and that each feature has the same impact on classification (i.e., it presents the Naive Bayes assumption). We can use Java's open source machine learning library Weka to implement the training of the Naive Bayes algorithm.

The following is a simple Java code implementation:

// 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();

In the above code, we first use Weka's DataSource class to load data from the training data file, and then use the BayesNet class to build naive Baye Si model. Finally, save the model to a file for later use.

3. Classify new texts

After we complete the training of the model, we can use the model to classify new texts and perform sentiment analysis. The following is a simple Java code implementation:

// 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));

In the above code, we first use Java's deserialization technology to load the model from the model file, and then build the instance to be classified. Note that the instances to be classified need to have the same attribute structure as the training data, otherwise errors will occur. Finally, the model is used for classification and the classification results are output.

4. Integrate into a Web application

If you want to integrate the sentiment analysis model into a Web application, you need to encapsulate the above code into an API and provide a Web interface for other programs Can use it.

Java provides many network programming libraries, such as: Servlet, JAX-RS, Spark, etc. In this article, we choose to use the technology provided by Spring Boot and Spring Web to quickly build a complete Web application.

First, we need to use Spring Boot's Maven plug-in to generate the skeleton of a web application. The command is as follows:

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

Then, integrate the previously mentioned sentiment analysis model into the web application. The following is a simple Java code implementation:

@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 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;
  }

}

In the above code, we first load the sentiment analysis model in the constructor of the class. Then, define a handler for HTTP POST requests to receive the text to be classified and return the classification results. In the processor, we first construct the instance to be classified, then use the model to classify, and finally return the classification result.

5. Deployment and Testing

After we have completed the implementation of the above code, we can use Maven to package it into an executable Jar package and run it on the server. For example, we can run the web application on the local computer using the following command:

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

We can then use a tool, such as Postman or curl, to send an HTTP POST request to the web application to test it. For example, we can use the following command to test the web application:

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！"}'

Note that we need to replace localhost:8080 in the above command with the IP address and port number of the server.

6. Summary

In this article, we introduced how to use Java to write an intelligent text classification system based on sentiment analysis. We first explained how to obtain text data suitable for sentiment analysis and use the Naive Bayes algorithm for model training. We then demonstrate how to use the trained model to classify and sentiment analyze new text. Finally, we integrated the model into a web application and provided a handler for HTTP POST requests for testing. This program is just a basic framework, and readers can expand it according to their own needs.

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