请使用给定的数据集，训练一个情感分析模型来分析电影评论的情感。输出应该是情感分析模型的准确率和混淆矩阵等评估指标。
- 电影评论数据集，包含正面和负面评价。
- 加载数据时要使用的参数。
以下是使用PyTorch训练情感分析模型的代码示例，使用的参数为`num_epochs=5`、`batch_size=32`、`learning_rate=0.001`：

```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import pandas as pd
from sklearn.metrics import confusion_matrix, accuracy_score


class SentimentAnalysisDataset(Dataset):
    def __init__(self, data_path):
        self.data = pd.read_csv(data_path)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        text = self.data.iloc[idx]["text"]
        label = self.data.iloc[idx]["label"]
        return text, label


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.embedding = nn.Embedding(num_embeddings=10000, embedding_dim=300)
        self.conv1 = nn.Conv1d(in_channels=300, out_channels=100, kernel_size=3)
        self.pool1 = nn.MaxPool1d(kernel_size=2)
        self.conv2 = nn.Conv1d(in_channels=100, out_channels=50, kernel_size=3)
        self.pool2 = nn.MaxPool1d(kernel_size=2)
        self.fc1 = nn.Linear(in_features=50*24, out_features=50)
        self.fc2 = nn.Linear(in_features=50, out_features=2)

    def forward(self, x):
        x = self.embedding(x)
        x = x.permute(0, 2, 1)
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 50*24)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


def train(model, train_loader, criterion, optimizer):
    model.train()
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()


def test(model, test_loader):
    model.eval()
    targets, predicts = [], []
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            _, pred = torch.max(output, 1)
            targets.extend(target.cpu().tolist())
            predicts.extend(pred.cpu().tolist())
    conf_mat = confusion_matrix(targets, predicts)
    accuracy = accuracy_score(targets, predicts)
    return conf_mat, accuracy


if __name__ == "__main__":
    data_path = "movie_reviews.csv"
    num_epochs = 5
    batch_size = 32
    learning_rate = 0.001

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    train_dataset = SentimentAnalysisDataset(data_path)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    model = Net().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(num_epochs):
        train(model, train_loader, criterion, optimizer)
        conf_mat, accuracy = test(model, train_loader)
        print("Epoch: {}, Accuracy: {}".format(epoch+1, accuracy))
        print("Confusion Matrix:
{}".format(conf_mat))
```

该模型使用了一个卷积神经网络和嵌入层。我们使用随机梯度下降和交叉熵损失函数进行训练。在每个epoch之后，我们评估模型性能并打印出准确度和混淆矩阵。

注意：您需要自行加载电影评论数据集并将其转换为适合此代码的格式。