SpringBoot integrates Java DL4J to implement text classification system

In today's era of information explosion, text classification in the field of natural language processing is particularly important.

Text classification can efficiently organize and manage massive text data. With the rapid development of the Internet, we are surrounded by a large amount of text information every day, from news reports, social media news to academic literature, business documents, etc. Without text classification, these data will be like a messy ocean, and it is difficult to quickly obtain valuable information from it. Through text classification, texts of different topics and types can be accurately divided, allowing users to quickly find the required content in specific categories, greatly improving the efficiency of information retrieval.

For businesses, text classification helps with precise marketing and customer service. Enterprises can classify customer feedback, evaluation and other texts to understand customer needs, satisfaction and potential problems. This not only allows timely adjustment of product and service strategies, but also improves customer experience and enhances the competitiveness of the company.

In the field of academic research, text classification can help researchers quickly screen relevant literature, focus on research on specific topics, and save a lot of time and energy. At the same time, the classification of literature in different disciplines and fields will also help promote the development of interdisciplinary research.

In addition, text classification also plays an important role in public opinion monitoring, information security, etc. Negative public opinion can be discovered and classified in a timely manner so that corresponding response measures can be taken. In the field of information security, classification of suspicious texts can help identify potential security threats.

This article will introduce how to use Spring Boot to integrate Java Deeplearning4j to build a text classification system, using news classification and email classification as examples.

1. Introduction

With the rapid development of information technology, we are exposed to a large amount of text data every day, such as news articles, emails, social media posts, etc. Classifying these text data can help us better understand and process them and improve the efficiency of information retrieval and management. Text classification systems can be applied to multiple fields, such as news media, e-commerce, financial services, etc.

2. Technical Overview

1. Neural Network Selection

In this text classification system, we chose to use Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM). The reasons for choosing LSTM are as follows:

• Processing sequence data:LSTM is very suitable for processing serial data such as text. It can capture long-term dependencies in text and is very helpful for understanding the context information of the text.
• Memory ability:LSTM has memory units that can remember long-term information and avoid gradient vanishing and gradient explosion problems in traditional RNNs.
• Wide application in natural language processing:LSTM has achieved great success in the field of natural language processing and has been widely used in tasks such as text classification, sentiment analysis, machine translation, etc.

2. Technology stack

• Spring Boot: An open source framework for building enterprise-level applications that provide fast development, automatic configuration and easy deployment features.
• Deeplearning4j: A Java-based deep learning library that supports a variety of neural network architectures, including LSTM, Convolutional Neural Network (CNN), etc.
• Java: A widely used programming language with a cross-platform and powerful ecosystem.

3. Dataset format

We will use two different datasets to train and test the text classification system, one is the news dataset and the other is the mail dataset.

1. News Dataset

The format of the news dataset is as follows:

News datasets can be stored in the form of CSV files, where each line represents a news article, including three fields: news title, news content, and category. The categories of news can be defined according to specific needs, such as political news, sports news, entertainment news, etc.

Here is a sample news dataset:

2. Mail dataset

The format of the mail dataset is as follows:

The mail dataset can be stored in the form of a CSV file, where each line represents a message and contains three fields: the mail subject, the mail content, and the category. The categories of emails can be defined according to specific needs, such as work emails, private emails, spam emails, etc.

Here is a sample mail dataset:

4. Maven dependency

In the project's file, you need to add the following Maven dependencies:

<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nlp</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId></groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

These dependencies will introduce the libraries related to Deeplearning4j and Spring Boot, allowing us to use their capabilities in our projects.

V. Code examples

1. Data preprocessing

Before we do text classification, we need to preprocess the data set, converting the text data into digital vectors so that the neural network can process them. Here is a sample code for data preprocessing:

import org.;
import org.;
import org.;
import org.;
import org.;

public class DataPreprocessor {

    public static DataSetIterator preprocessData(String filePath) {
        // Create TokenizerFactory        TokenizerFactory tokenizerFactory = new UimaTokenizerFactory();

        // Create a document vector        DocumentVectorizer documentVectorizer = new ()
               .setTokenizerFactory(tokenizerFactory)
               .build();

        // Load the dataset        InMemoryDataSetIterator dataSetIterator = new ()
               .addSource(filePath, documentVectorizer)
               .build();

        // Data standardization        DataNormalization normalizer = new NormalizerStandardize();
        (dataSetIterator);
        (normalizer);

        return dataSetIterator;
    }
}

In the above code, we first create aTokenizerFactory, used to convert text data into word vectors. Then, we useDocumentVectorizerConvert word vectors to document vectors and useInMemoryDataSetIteratorLoad the dataset. Finally, we useNormalizerStandardizeThe data is standardized so that the mean value of the data is 0 and the standard deviation is 1.

2. Model construction

Next, we need to build an LSTM model for text classification. Here is a sample code for model building:

import org.;
import org.;
import org.;
import org.;
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import org.;
import org.;

public class TextClassificationModel {

    public static MultiLayerNetwork buildModel(int inputSize, int numClasses) {
        // Build neural network configuration        MultiLayerConfiguration configuration = new ()
               .seed(12345)
               .weightInit()
               .updater()
               .list()
               .layer(0, new ()
                       .nIn(inputSize)
                       .nOut(128)
                       .activation()
                       .build())
               .layer(1, new ()
                       .activation()
                       .nOut(numClasses)
                       .build())
               .build();

        // Create a neural network model        MultiLayerNetwork model = new MultiLayerNetwork(configuration);
        ();

        return model;
    }
}

In the above code, we useTo build a neural network configuration. We added an LSTM layer and an output layer and set the corresponding parameters. Finally, we useMultiLayerNetworkCreate a neural network model and initialize the model.

3. Training the model

We then need to use the preprocessed dataset to train the model. Here is a sample code for training a model:

import org.;
import org.;
import org.;
import org.;

public class ModelTrainer {

    public static void trainModel(MultiLayerNetwork model, DataSetIterator iterator, int numEpochs) {
        // Set optimization algorithm and learning rate        (OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT);
        (0.01);

        // Add training listener        (new ScoreIterationListener(100));

        // Training the model        for (int epoch = 0; epoch < numEpochs; epoch++) {
            (iterator);
            ("Epoch " + epoch + " completed.");
        }
    }
}

In the above code, we first set the optimization algorithm and learning rate of the model. We then add a training listener to output the loss value during the training process. Finally, we use()Methods to train the model and output the completion information of each epoch.

4. Predicted results

Finally, we can use the trained model to predict the categories of new text data. Here is a sample code for predicting results:

import org.;
import org.;
import org.;

public class ModelPredictor {

    public static String predictCategory(MultiLayerNetwork model, String text) {
        // Preprocess text data        DataSet dataSet = (text);

        // Prediction category        INDArray output = (());
        int predictedClass = argMax(output);

        // Return the category name        return getCategoryName(predictedClass);
    }

    private static int argMax(INDArray array) {
        double maxValue = Double.NEGATIVE_INFINITY;
        int maxIndex = -1;
        for (int i = 0; i < (); i++) {
            if ((i) > maxValue) {
                maxValue = (i);
                maxIndex = i;
            }
        }
        return maxIndex;
    }

    private static String getCategoryName(int classIndex) {
        // Return the category name according to the category index        switch (classIndex) {
            case 0:
                return "Political News";
            case 1:
                return "Sports News";
            case 2:
                return "Entertainment News";
            default:
                return "Unknown Category";
        }
    }
}

In the above code, we first use()Method preprocesses the input text data. Then, we use()Methods to predict the category of text data. Finally, we return the corresponding category name based on the prediction results.

VI. Unit Test

To ensure the correctness of the code, we can write unit tests to test various parts of the text classification system. Here is a sample code for a unit test:

import org.;
import ;
import ;
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import static ;

public class TextClassificationSystemTest {

    private MultiLayerNetwork model;
    private DataSetIterator iterator;

    @BeforeEach
    public void setUp() {
        // Load the dataset and preprocess it        iterator = ("path/to/");

        // Build the model        model = ((), ());
    }

    @Test
    public void testModelTraining() {
        // Training the model        (model, iterator, 10);

        // Predicted results        String text = "U.S. President Biden delivered an important speech";
        String predictedCategory = (model, text);

        // Verify the prediction results        assertEquals("Political News", predictedCategory);
    }
}

In the above code, we firstsetUp()The data set is loaded, the data is preprocessed, and the model is constructed. Then, intestModelTraining()The model is trained in the method, and a new text data is used to predict it, and finally verify whether the prediction results are correct.

7. Expected output

When running unit tests, the expected output is as follows:

Epoch 0 completed.
Epoch 1 completed.
...
Epoch 9 completed.

If the prediction results are correct, the unit test will pass and no error message will be output.

8. Conclusion

This article describes how to use Spring Boot to integrate Deeplearning4j to build a text classification system. We chose LSTM as the neural network architecture because it can effectively process the serial data of text such as text and capture long-term dependencies in text. We also introduce the format of the dataset, Maven dependencies, code examples, unit tests, and expected output. Through this text classification system, we can divide text data into different categories for easy management and retrieval.

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