机器学习：使用LSTM训练情感分析模型

• 能够利用大模型解决相关实际项目需求：大数据时代，越来越多的企业和机构需要处理海量数据，利用大模型技术可以更好地处理这些数据，提高数据分析和决策的准确性。• 基于大模型和企业数据AI应用开发，实现大模型理论、掌握GPU算力、硬件、LangChain开发框架和项目实战技能，学会Fine-tuning垂直训练大模型（数据准备、数据蒸馏、大模型部署）一站式掌握；至此，根据训练集训练的模型保存完毕。

码农Q！

918人浏览 · 2025-07-29 14:30:31

码农Q！ · 2025-07-29 14:30:31 发布

背景：电商平台收到了许多关于宝贝的评论，需要自动从大量的数据中分析出各种情感的用户评论，并实时给出预警和提醒。

数据格式：

数据中最重要的两列：Review Text 和Rating，一个是评论内容一个是评分。

第一步：先进行数据处理：

def load_and_preprocess_data(filepath):
    """加载并预处理数据"""
    df = pd.read_csv(filepath)
    texts = df['Review Text'].values
    labels = df['Rating'].values

    # 标签编码 (1-5 -> 0-4)
    le = LabelEncoder()
    labels = le.fit_transform(labels)

    # 文本序列化
    tokenizer = Tokenizer(num_words=20000, oov_token="<OOV>")
    tokenizer.fit_on_texts(texts)
    sequences = tokenizer.texts_to_sequences(texts)
    padded_sequences = pad_sequences(sequences, maxlen=200, truncating='post')

    return padded_sequences, labels, tokenizer, le

加载数据，并将数据序列化。

第二步：利用LSTM建立模型的结构，设置数据嵌入的向量维度：

def build_model(vocab_size, max_len, embedding_dim=128):
    """构建LSTM模型"""
    model = Sequential([
        Embedding(vocab_size, embedding_dim, input_length=max_len),
        Bidirectional(LSTM(64, return_sequences=True)),
        Dropout(0.5),
        LSTM(32),
        Dropout(0.5),
        Dense(64, activation='relu'),
        Dropout(0.5),
        Dense(5, activation='softmax')
    ])

    model.compile(
        optimizer=Adam(learning_rate=0.001),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    return model

第三步：训练并保存模型：

def main():
    # 加载数据
    X, y, tokenizer, le = load_and_preprocess_data('Clothing_Reviews.csv')
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # 构建模型
    model = build_model(vocab_size=20000, max_len=200)
    model.summary()

    # 训练模型
    history = model.fit(
        X_train, y_train,
        validation_data=(X_test, y_test),
        epochs=10,
        batch_size=64,
        callbacks=[EarlyStopping(monitor='val_loss', patience=3)],
        verbose=1
    )
    print(f"history: {history}")
    # 评估模型
    # plot_history(history)
    loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
    print(f'测试集准确率: {accuracy:.2f}')

    # 保存模型和tokenizer
    model.save('sentiment_lstm_5class.h5')
    with open('tokenizer.pickle', 'wb') as handle:
        pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)
    print("模型和tokenizer已保存")
    # 保存label encoder
    with open('label_encoder.pickle', 'wb') as handle:
        pickle.dump(le, handle, protocol=pickle.HIGHEST_PROTOCOL)

至此，根据训练集训练的模型保存完毕。要想使用训练保持好的模型进行数据预测，需要新建一个预测的方法：

import numpy as np
import pickle

from keras.src.saving import load_model
from keras.src.utils import pad_sequences
from sklearn.preprocessing import LabelEncoder


class SentimentPredictor:
    def __init__(self, model_path, tokenizer_path, label_encoder_path=None, max_len=200):
        """初始化预测器"""
        self.model = load_model(model_path)
        with open(tokenizer_path, 'rb') as handle:
            self.tokenizer = pickle.load(handle)
        with open(label_encoder_path, 'rb') as handle:
            self.label_encoder = pickle.load(handle)
        self.max_len = max_len

    def preprocess_text(self, text):
        """预处理文本"""
        sequence = self.tokenizer.texts_to_sequences([text])
        padded = pad_sequences(sequence, maxlen=self.max_len, truncating='post')
        return padded

    def predict_sentiment(self, text, verbose=False):
        """预测情感分值(1-5)"""
        # 预处理
        padded_sequence = self.preprocess_text(text)

        # 预测
        prediction = self.model.predict(padded_sequence, verbose=0)
        predicted_class = np.argmax(prediction, axis=1)

        # 转换回原始标签(1-5)
        predicted_score = self.label_encoder.inverse_transform(predicted_class)[0]

        if verbose:
            print(f"评论: {text}")
            print(f"预测情感分值: {predicted_score}")
            print("各类别概率:")
            for i, prob in enumerate(prediction[0]):
                print(f"{i + 1}分: {prob:.4f}")
            print("-" * 50)

        return predicted_score, prediction[0]

这个类封装了读取加载模型、使用模型进行预测的方法，调用是传入已经保持的模型路径即可。使用实例：

# 初始化预测器
predictor = SentimentPredictor(
    model_path='sentiment_lstm_5class.h5',
    tokenizer_path='tokenizer.pickle',
    label_encoder_path='label_encoder.pickle'
)

# 测试评论
test_reviews = [
    "This product is absolutely amazing! Best purchase ever!",
    "The item was okay, but not worth the price.",
    "Terrible quality. Would not recommend to anyone.",
    "It's decent for the price, though it has some flaws.",
    "I'm completely satisfied with this purchase. It exceeded all my expectations!"
]

# 批量预测
for review in test_reviews:
    score, probs = predictor.predict_sentiment(review, verbose=True)
    print(f"{review}: 预测情感分值: {score},准确率： {np.max(probs) * 100:.2f}%")

# 预测单个评论
sample_review = "The product was good but the delivery took too long."
score, probs = predictor.predict_sentiment(sample_review, verbose=True)
print(f"{sample_review}: 预测情感分值: {score},准确率： {np.max(probs) * 100:.2f}%")