<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>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

import java.util.ArrayList;
import java.util.List;

import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;

public class DataPreprocessing {

    public static List<String[]> preprocessData(List<String> rawData) {
        List<String[]> processedData = new ArrayList<>();
        TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();

        for (String rawText : rawData) {
            // 文本清洗
            String cleanedText = cleanText(rawText);

            // 分詞
            String[] tokens = tokenizerFactory.create(cleanedText).getTokens();

            // 添加到處理后的數(shù)據(jù)列表
            processedData.add(tokens);
        }

        return processedData;
    }

    private static String cleanText(String text) {
        // 去除標(biāo)點(diǎn)符號(hào)、特殊字符和停用詞等
        return text.replaceAll("[^a-zA-Z0-9 ]", "").toLowerCase();
    }
}

import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class SentimentAnalysisModel {

    public static MultiLayerNetwork buildModel(int inputSize, int hiddenSize, int outputSize) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
               .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
               .updater(org.deeplearning4j.nn.conf.Updater.RMSPROP)
               .list()
               .layer(0, new LSTM.Builder().nIn(inputSize).nOut(hiddenSize).activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
               .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(hiddenSize).nOut(outputSize).build())
               .pretrain(false).backprop(true).build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();

        return model;
    }
}

import java.util.List;

import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class ModelTraining {

    public static void trainModel(MultiLayerNetwork model, List<String[]> trainingData, int numEpochs) {
        // 將訓(xùn)練數(shù)據(jù)轉(zhuǎn)換為數(shù)據(jù)集
        DataSet trainingSet = convertToDataSet(trainingData);

        // 添加訓(xùn)練監(jiān)聽器
        model.setListeners(new ScoreIterationListener(100));

        for (int epoch = 0; epoch < numEpochs; epoch++) {
            model.fit(trainingSet);
            System.out.println("Epoch " + epoch + " completed.");
        }
    }

    private static DataSet convertToDataSet(List<String[]> data) {
        int numExamples = data.size();
        int maxSequenceLength = findMaxSequenceLength(data);
        int inputSize = findInputSize(data);

        INDArray input = Nd4j.zeros(numExamples, maxSequenceLength, inputSize);
        INDArray labels = Nd4j.zeros(numExamples, 3); // 假設(shè)情感標(biāo)簽有三種：積極、消極、中性

        for (int i = 0; i < numExamples; i++) {
            String[] tokens = data.get(i);
            int sequenceLength = tokens.length;

            for (int j = 0; j < sequenceLength; j++) {
                // 將單詞轉(zhuǎn)換為向量表示，并填充到輸入矩陣中
                input.putScalar(new int[]{i, j, getWordIndex(tokens[j])}, 1.0);
            }

            // 設(shè)置標(biāo)簽
            int labelIndex = getLabelIndex(data.get(i));
            labels.putScalar(new int[]{i, labelIndex}, 1.0);
        }

        return new DataSet(input, labels);
    }

    private static int findMaxSequenceLength(List<String[]> data) {
        int maxLength = 0;
        for (String[] tokens : data) {
            maxLength = Math.max(maxLength, tokens.length);
        }
        return maxLength;
    }

    private static int findInputSize(List<String[]> data) {
        // 假設(shè)使用詞袋模型，輸入大小為不同單詞的數(shù)量
        return findUniqueWords(data).size();
    }

    private static List<String> findUniqueWords(List<String[]> data) {
        List<String> uniqueWords = new ArrayList<>();
        for (String[] tokens : data) {
            for (String token : tokens) {
                if (!uniqueWords.contains(token)) {
                    uniqueWords.add(token);
                }
            }
        }
        return uniqueWords;
    }

    private static int getWordIndex(String word) {
        // 根據(jù)單詞列表返回單詞的索引
        return findUniqueWords().indexOf(word);
    }

    private static int getLabelIndex(String[] tokens) {
        // 根據(jù)情感標(biāo)簽返回標(biāo)簽的索引
        String label = tokens[tokens.length - 1];
        if (label.equals("積極")) {
            return 0;
        } else if (label.equals("消極")) {
            return 1;
        } else {
            return 2;
        }
    }
}

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

public class SentimentPrediction {

    public static String predictSentiment(MultiLayerNetwork model, String text) {
        // 預(yù)處理文本
        String[] tokens = preprocessText(text);

        // 將文本轉(zhuǎn)換為向量表示
        INDArray input = Nd4j.zeros(1, tokens.length, findInputSize());
        for (int i = 0; i < tokens.length; i++) {
            input.putScalar(new int[]{0, i, getWordIndex(tokens[i])}, 1.0);
        }

        // 進(jìn)行預(yù)測(cè)
        INDArray output = model.output(input);

        // 返回預(yù)測(cè)的情感標(biāo)簽
        int labelIndex = Nd4j.argMax(output, 1).getInt(0);
        if (labelIndex == 0) {
            return "積極";
        } else if (labelIndex == 1) {
            return "消極";
        } else {
            return "中性";
        }
    }

    private static String[] preprocessText(String text) {
        // 文本清洗、分詞等預(yù)處理步驟
        return text.replaceAll("[^a-zA-Z0-9 ]", "").toLowerCase().split(" ");
    }

    private static int findInputSize() {
        // 假設(shè)使用詞袋模型，輸入大小為不同單詞的數(shù)量
        return findUniqueWords().size();
    }

    private static int getWordIndex(String word) {
        // 根據(jù)單詞列表返回單詞的索引
        return findUniqueWords().indexOf(word);
    }

    private static List<String> findUniqueWords() {
        // 假設(shè)在訓(xùn)練階段已經(jīng)計(jì)算了不同單詞的列表
        return null;
    }
}

import java.util.ArrayList;
import java.util.List;

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;

import static org.junit.jupiter.api.Assertions.assertEquals;

public class SentimentAnalysisTest {

    private List<String> rawData;
    private MultiLayerNetwork model;

    @BeforeEach
    public void setup() {
        // 準(zhǔn)備測(cè)試數(shù)據(jù)
        rawData = new ArrayList<>();
        rawData.add("這個(gè)產(chǎn)品非常好用，我很滿意。 積極");
        rawData.add("這個(gè)服務(wù)態(tài)度太差了，很不滿意。 消極");
        rawData.add("這個(gè)產(chǎn)品一般般，沒有特別的感覺。 中性");

        // 構(gòu)建和訓(xùn)練模型
        model = SentimentAnalysisModel.buildModel(10, 50, 3);
        ModelTraining.trainModel(model, DataPreprocessing.preprocessData(rawData), 10);
    }

    @Test
    public void testPredictSentiment() {
        String text = "這個(gè)產(chǎn)品還不錯(cuò)。";
        String predictedSentiment = SentimentPrediction.predictSentiment(model, text);
        assertEquals("積極", predictedSentiment);
    }
}