基于BERT的情感分析

1. 项目背景

情感分析（Sentiment Analysis）是自然语言处理的重要应用之一，用于判断文本的情感倾向，如正面、负面或中性。随着深度学习的发展，预训练语言模型如BERT在各种自然语言处理任务中取得了显著的效果。本项目利用预训练语言模型BERT，构建一个能够对文本进行情感分类的模型。

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2. 项目结构

sentiment-analysis/
├── data/
│   ├── train.csv        # 训练数据集
│   ├── test.csv         # 测试数据集
├── src/
│   ├── preprocess.py    # 数据预处理模块
│   ├── train.py         # 模型训练脚本
│   ├── evaluate.py      # 模型评估脚本
│   ├── inference.py     # 模型推理脚本
│   ├── utils.py         # 工具函数（可选）
├── models/
│   ├── bert_model.pt    # 保存的模型权重
├── logs/
│   ├── training.log     # 训练日志（可选）
├── README.md            # 项目说明文档
├── requirements.txt     # 依赖包列表
└── run.sh               # 一键运行脚本

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3. 环境准备

3.1 系统要求

• Python 3.6 或以上版本
• GPU（可选，但建议使用以加速训练）

3.2 安装依赖

建议在虚拟环境中运行。安装所需的依赖包：

pip install -r requirements.txt

requirements.txt内容：

torch>=1.7.0
transformers>=4.0.0
pandas
scikit-learn
tqdm

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4. 数据准备

4.1 数据格式

数据文件train.csv和test.csv的格式如下：

text	label
I love this product.	1
This is a bad movie.	0

• text：输入文本
• label：目标标签，1为正面情感，0为负面情感

将数据文件保存至data/目录下。

4.2 数据集划分

可以使用train_test_split将数据划分为训练集和测试集。

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5. 代码实现

5.1 数据预处理 (src/preprocess.py)

import pandas as pd
from transformers import BertTokenizer
from torch.utils.data import Dataset
import torchclass SentimentDataset(Dataset):"""自定义的用于情感分析的Dataset。"""def __init__(self, data_path, tokenizer, max_len=128):"""初始化Dataset。Args:data_path (str): 数据文件的路径。tokenizer (BertTokenizer): BERT的分词器。max_len (int): 最大序列长度。"""self.data = pd.read_csv(data_path)self.tokenizer = tokenizerself.max_len = max_lendef __len__(self):"""返回数据集的大小。"""return len(self.data)def __getitem__(self, idx):"""根据索引返回一条数据。Args:idx (int): 数据索引。Returns:dict: 包含input_ids、attention_mask和label的字典。"""text = str(self.data.iloc[idx]['text'])label = int(self.data.iloc[idx]['label'])encoding = self.tokenizer(text, padding='max_length', truncation=True, max_length=self.max_len, return_tensors="pt")return {'input_ids': encoding['input_ids'].squeeze(0),  # shape: [seq_len]'attention_mask': encoding['attention_mask'].squeeze(0),  # shape: [seq_len]'label': torch.tensor(label, dtype=torch.long)  # shape: []}

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5.2 模型训练 (src/train.py)

import torch
from torch.utils.data import DataLoader
from transformers import BertForSequenceClassification, AdamW, BertTokenizer, get_linear_schedule_with_warmup
from preprocess import SentimentDataset
import argparse
import os
from tqdm import tqdmdef train_model(data_path, model_save_path, batch_size=16, epochs=3, lr=2e-5, max_len=128):"""训练BERT情感分析模型。Args:data_path (str): 训练数据的路径。model_save_path (str): 模型保存的路径。batch_size (int): 批次大小。epochs (int): 训练轮数。lr (float): 学习率。max_len (int): 最大序列长度。"""# 初始化分词器和数据集tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')dataset = SentimentDataset(data_path, tokenizer, max_len=max_len)# 划分训练集和验证集train_size = int(0.8 * len(dataset))val_size = len(dataset) - train_sizetrain_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])# 数据加载器train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)val_loader = DataLoader(val_dataset, batch_size=batch_size)# 初始化模型model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)# 优化器和学习率调度器optimizer = AdamW(model.parameters(), lr=lr)total_steps = len(train_loader) * epochsscheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)# 设备设置device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device)# 训练循环for epoch in range(epochs):model.train()total_loss = 0progress_bar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}")for batch in progress_bar:optimizer.zero_grad()input_ids = batch['input_ids'].to(device)  # shape: [batch_size, seq_len]attention_mask = batch['attention_mask'].to(device)  # shape: [batch_size, seq_len]labels = batch['label'].to(device)  # shape: [batch_size]outputs = model(input_ids, attention_mask=attention_mask, labels=labels)loss = outputs.lossloss.backward()optimizer.step()scheduler.step()total_loss += loss.item()progress_bar.set_postfix(loss=loss.item())avg_train_loss = total_loss / len(train_loader)print(f"Epoch {epoch + 1}/{epochs}, Average Loss: {avg_train_loss:.4f}")# 验证模型model.eval()val_loss = 0correct = 0total = 0with torch.no_grad():for batch in val_loader:input_ids = batch['input_ids'].to(device)attention_mask = batch['attention_mask'].to(device)labels = batch['label'].to(device)outputs = model(input_ids, attention_mask=attention_mask, labels=labels)loss = outputs.losslogits = outputs.logitsval_loss += loss.item()preds = torch.argmax(logits, dim=1)correct += (preds == labels).sum().item()total += labels.size(0)avg_val_loss = val_loss / len(val_loader)val_accuracy = correct / totalprint(f"Validation Loss: {avg_val_loss:.4f}, Accuracy: {val_accuracy:.4f}")# 保存模型os.makedirs(os.path.dirname(model_save_path), exist_ok=True)torch.save(model.state_dict(), model_save_path)print(f"Model saved to {model_save_path}")if __name__ == "__main__":parser = argparse.ArgumentParser(description="Train BERT model for sentiment analysis")parser.add_argument('--data_path', type=str, default='data/train.csv', help='Path to training data')parser.add_argument('--model_save_path', type=str, default='models/bert_model.pt', help='Path to save the trained model')parser.add_argument('--batch_size', type=int, default=16, help='Batch size')parser.add_argument('--epochs', type=int, default=3, help='Number of training epochs')parser.add_argument('--lr', type=float, default=2e-5, help='Learning rate')parser.add_argument('--max_len', type=int, default=128, help='Maximum sequence length')args = parser.parse_args()train_model(data_path=args.data_path,model_save_path=args.model_save_path,batch_size=args.batch_size,epochs=args.epochs,lr=args.lr,max_len=args.max_len)

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5.3 模型评估 (src/evaluate.py)

import torch
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from preprocess import SentimentDataset
from torch.utils.data import DataLoader
from transformers import BertForSequenceClassification, BertTokenizer
import argparse
from tqdm import tqdmdef evaluate_model(data_path, model_path, batch_size=16, max_len=128):"""评估BERT情感分析模型。Args:data_path (str): 测试数据的路径。model_path (str): 训练好的模型的路径。batch_size (int): 批次大小。max_len (int): 最大序列长度。"""# 初始化分词器和数据集tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')dataset = SentimentDataset(data_path, tokenizer, max_len=max_len)loader = DataLoader(dataset, batch_size=batch_size)# 加载模型model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))model.eval()# 设备设置device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device)all_preds = []all_labels = []with torch.no_grad():for batch in tqdm(loader, desc="Evaluating"):input_ids = batch['input_ids'].to(device)attention_mask = batch['attention_mask'].to(device)labels = batch['label'].to(device)outputs = model(input_ids, attention_mask=attention_mask)logits = outputs.logitspreds = torch.argmax(logits, dim=1)all_preds.extend(preds.cpu().numpy())all_labels.extend(labels.cpu().numpy())accuracy = accuracy_score(all_labels, all_preds)precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='binary')print(f"Accuracy: {accuracy:.4f}")print(f"Precision: {precision:.4f}, Recall: {recall:.4f}, F1-score: {f1:.4f}")if __name__ == "__main__":parser = argparse.ArgumentParser(description="Evaluate BERT model for sentiment analysis")parser.add_argument('--data_path', type=str, default='data/test.csv', help='Path to test data')parser.add_argument('--model_path', type=str, default='models/bert_model.pt', help='Path to the trained model')parser.add_argument('--batch_size', type=int, default=16, help='Batch size')parser.add_argument('--max_len', type=int, default=128, help='Maximum sequence length')args = parser.parse_args()evaluate_model(data_path=args.data_path,model_path=args.model_path,batch_size=args.batch_size,max_len=args.max_len)

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5.4 推理 (src/inference.py)

import torch
from transformers import BertTokenizer, BertForSequenceClassification
import argparsedef predict_sentiment(text, model_path, max_len=128):"""对输入的文本进行情感预测。Args:text (str): 输入的文本。model_path (str): 训练好的模型的路径。max_len (int): 最大序列长度。Returns:str: 预测的情感类别。"""# 初始化分词器和模型tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))model.eval()# 设备设置device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device)# 数据预处理inputs = tokenizer(text, return_tensors="pt", truncation=True, padding='max_length', max_length=max_len)inputs = {key: value.to(device) for key, value in inputs.items()}# 模型推理with torch.no_grad():outputs = model(**inputs)logits = outputs.logitsprediction = torch.argmax(logits, dim=1).item()sentiment = "Positive" if prediction == 1 else "Negative"return sentimentif __name__ == "__main__":parser = argparse.ArgumentParser(description="Inference script for sentiment analysis")parser.add_argument('--text', type=str, required=True, help='Input text for sentiment prediction')parser.add_argument('--model_path', type=str, default='models/bert_model.pt', help='Path to the trained model')parser.add_argument('--max_len', type=int, default=128, help='Maximum sequence length')args = parser.parse_args()sentiment = predict_sentiment(text=args.text,model_path=args.model_path,max_len=args.max_len)print(f"Input Text: {args.text}")print(f"Predicted Sentiment: {sentiment}")

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6. 项目运行

6.1 一键运行脚本 (run.sh)

#!/bin/bash# 训练模型
python src/train.py --data_path=data/train.csv --model_save_path=models/bert_model.pt# 评估模型
python src/evaluate.py --data_path=data/test.csv --model_path=models/bert_model.pt# 推理示例
python src/inference.py --text="I love this movie!" --model_path=models/bert_model.pt

6.2 单独运行

6.2.1 训练模型

python src/train.py --data_path=data/train.csv --model_save_path=models/bert_model.pt --epochs=3 --batch_size=16

6.2.2 评估模型

python src/evaluate.py --data_path=data/test.csv --model_path=models/bert_model.pt

6.2.3 模型推理

python src/inference.py --text="This product is great!" --model_path=models/bert_model.pt

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7. 结果展示

7.1 训练结果

• 损失下降曲线：可以使用matplotlib或tensorboard绘制训练过程中的损失变化。
• 训练日志：在logs/training.log中记录训练过程。

7.2 模型评估

• 准确率（Accuracy）：模型在测试集上的准确率。
• 精确率、召回率、F1-score：更全面地评估模型性能。

7.3 推理示例

示例：

python src/inference.py --text="I absolutely love this!" --model_path=models/bert_model.pt

输出：

Input Text: I absolutely love this!
Predicted Sentiment: Positive

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8. 注意事项

• 模型保存与加载：确保模型保存和加载时的路径正确，特别是在使用相对路径时。
• 设备兼容性：代码中已考虑CPU和GPU的兼容性，确保设备上安装了相应的PyTorch版本。
• 依赖版本：依赖的库版本可能会影响代码运行，建议使用requirements.txt中指定的版本。

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9. 参考资料

• BERT论文
• Hugging Face Transformers文档
• PyTorch官方文档