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【自然语言处理（NLP）实战】LSTM网络实现中文文本情感分析（手把手与教学超详细）_自然语言处理实战 下载

作者：不正经 | 2024-02-09 19:36:07

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自然语言处理实战 下载

目录

引言：

1.所有文件展示：

1.中文停用词数据（hit_stopwords.txt)来源于：

2.其中data数据集为chinese_text_cnn-master.zip提取出的文件。点击链接进入github，点击Code、Download ZIP即可下载。

2.安装依赖库：

3.数据预处理（data_set.py)：

train.txt-去除停用词后的训练集文件：

test.txt -去除停用词后的测试集文件：

4. 模型训练以及保存（main.py)

1.LSTM模型搭建：

2.main.py代价展示 ：

 3.模型保存

4.训练结果 

5.LSTM模型测试（test.py) 

 1.测试结果：

2.测试结果：

6.完整代码展示：

1.data_set.py

2.mian.py

3.test.py 

引言：

在当今数字化时代，人们在社交媒体、评论平台以及各类在线交流中产生了海量的文本数据。这些数据蕴含着丰富的情感信息，从而成为了深入理解用户态度、市场趋势，甚至社会情绪的宝贵资源。自然语言处理（NLP）的发展为我们提供了强大的工具，使得对文本情感进行分析成为可能。在这个领域中，长短时记忆网络（LSTM）凭借其能够捕捉文本序列中长距离依赖关系的能力，成为了情感分析任务中的一项重要技术。

本篇博客将手把手地教你如何使用LSTM网络实现中文文本情感分析。我们将从数据预处理开始，逐步构建一个端到端的情感分析模型。通过详细的步骤和示例代码，深入了解如何处理中文文本数据、构建LSTM模型、进行训练和评估。

1.所有文件展示：

1.中文停用词数据（hit_stopwords.txt)来源于：

项目目录预览 - stopwords - GitCode

2.其中data数据集为chinese_text_cnn-master.zip提取出的文件。点击链接进入github，点击Code、Download ZIP即可下载。

2.安装依赖库：

  1. pip install torch # 搭建LSTM模型
  2. pip install gensim # 中文文本词向量转换
  3. pip install numpy # 数据清洗、预处理
  4. pip install pandas

3.数据预处理（data_set.py)：

  1. # -*- coding: utf-8 -*-
  2. # @Time : 2023/11/15 10:52
  3. # @Author :Muzi
  4. # @File : data_set.py
  5. # @Software: PyCharm
  6. import pandas as pd
  7. import jieba
  8.  
  9.  
  10. # 数据读取
  11. def load_tsv(file_path):
  12.     data = pd.read_csv(file_path, sep='\t')
  13.     data_x = data.iloc[:, -1]
  14.     data_y = data.iloc[:, 1]
  15.     return data_x, data_y
  16.  
  17. train_x, train_y = load_tsv("./data/train.tsv")
  18. test_x, test_y = load_tsv("./data/test.tsv")
  19. train_x=[list(jieba.cut(x)) for x in train_x]
  20. test_x=[list(jieba.cut(x)) for x in test_x]
  21.  
  22. with open('./hit_stopwords.txt','r',encoding='UTF8') as f:
  23.     stop_words=[word.strip() for word in f.readlines()]
  24.     print('Successfully')
  25. def drop_stopword(datas):
  26.     for data in datas:
  27.         for word in data:
  28.             if word in stop_words:
  29.                 data.remove(word)
  30.     return datas
  31.  
  32. def save_data(datax,path):
  33.     with open(path, 'w', encoding="UTF8") as f:
  34.         for lines in datax:
  35.             for i, line in enumerate(lines):
  36.                 f.write(str(line))
  37.                 # 如果不是最后一行，就添加一个逗号
  38.                 if i != len(lines) - 1:
  39.                     f.write(',')
  40.             f.write('\n')
  41.  
  42. if __name__ == '__main':
  43.     train_x=drop_stopword(train_x)
  44.     test_x=drop_stopword(test_x)
  45.  
  46.     save_data(train_x,'./train.txt')
  47.     save_data(test_x,'./test.txt')
  48.     print('Successfully')

train.txt-去除停用词后的训练集文件：

 

test.txt -去除停用词后的测试集文件：

4. 模型训练以及保存（main.py)

1.LSTM模型搭建：

不同的数据集应该有不同的分类标准，我这里用到的数据模型属于二分类问题

  1. # 定义LSTM模型
  2. class LSTMModel(nn.Module):
  3.     def __init__(self, input_size, hidden_size, output_size):
  4.         super(LSTMModel, self).__init__()
  5.         self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
  6.         self.fc = nn.Linear(hidden_size, output_size)
  7.  
  8.     def forward(self, x):
  9.         lstm_out, _ = self.lstm(x)
  10.         output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
  11.         return output
  12.  
  13. # 定义模型
  14. input_size = word2vec_model.vector_size
  15. hidden_size = 50  # 你可以根据需要调整隐藏层大小
  16. output_size = 2  # 输出的大小，根据你的任务而定
  17.  
  18. model = LSTMModel(input_size, hidden_size, output_size)
  19. # 定义损失函数和优化器
  20. criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
  21. optimizer = torch.optim.Adam(model.parameters(), lr=0.0002)

2.main.py代价展示 ：

  1. # -*- coding: utf-8 -*-
  2. # @Time : 2023/11/13 20:31
  3. # @Author :Muzi
  4. # @File : mian.py.py
  5. # @Software: PyCharm
  6. import pandas as pd
  7. import torch
  8. from torch import nn
  9. import jieba
  10. from gensim.models import Word2Vec
  11. import numpy as np
  12. from data_set import load_tsv
  13. from torch.utils.data import DataLoader, TensorDataset
  14.  
  15.  
  16. # 数据读取
  17. def load_txt(path):
  18.     with open(path,'r',encoding='utf-8') as f:
  19.         data=[[line.strip()] for line in f.readlines()]
  20.         return data
  21.  
  22. train_x=load_txt('train.txt')
  23. test_x=load_txt('test.txt')
  24. train=train_x+test_x
  25. X_all=[i for x in train for i in x]
  26.  
  27. _, train_y = load_tsv("./data/train.tsv")
  28. _, test_y = load_tsv("./data/test.tsv")
  29. # 训练Word2Vec模型
  30. word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
  31.  
  32. # 将文本转换为Word2Vec向量表示
  33. def text_to_vector(text):
  34.     vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
  35.     return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
  36.  
  37. X_train_w2v = [[text_to_vector(text)] for line in train_x for text in line]
  38. X_test_w2v = [[text_to_vector(text)] for line in test_x for text in line]
  39.  
  40. # 将词向量转换为PyTorch张量
  41. X_train_array = np.array(X_train_w2v, dtype=np.float32)
  42. X_train_tensor = torch.Tensor(X_train_array)
  43. X_test_array = np.array(X_test_w2v, dtype=np.float32)
  44. X_test_tensor = torch.Tensor(X_test_array)
  45. #使用DataLoader打包文件
  46. train_dataset = TensorDataset(X_train_tensor, torch.LongTensor(train_y))
  47. train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
  48. test_dataset = TensorDataset(X_test_tensor,torch.LongTensor(test_y))
  49. test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)
  50. # 定义LSTM模型
  51. class LSTMModel(nn.Module):
  52.     def __init__(self, input_size, hidden_size, output_size):
  53.         super(LSTMModel, self).__init__()
  54.         self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
  55.         self.fc = nn.Linear(hidden_size, output_size)
  56.  
  57.     def forward(self, x):
  58.         lstm_out, _ = self.lstm(x)
  59.         output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
  60.         return output
  61.  
  62. # 定义模型
  63. input_size = word2vec_model.vector_size
  64. hidden_size = 50  # 你可以根据需要调整隐藏层大小
  65. output_size = 2  # 输出的大小，根据你的任务而定
  66.  
  67. model = LSTMModel(input_size, hidden_size, output_size)
  68. # 定义损失函数和优化器
  69. criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
  70. optimizer = torch.optim.Adam(model.parameters(), lr=0.0002)
  71.  
  72. if __name__ == "__main__":
  73.     # 训练模型
  74.     num_epochs = 10
  75.     log_interval = 100  # 每隔100个批次输出一次日志
  76.     loss_min=100
  77.     for epoch in range(num_epochs):
  78.         model.train()
  79.         for batch_idx, (data, target) in enumerate(train_loader):
  80.             outputs = model(data)
  81.             loss = criterion(outputs, target)
  82.  
  83.             optimizer.zero_grad()
  84.             loss.backward()
  85.             optimizer.step()
  86.  
  87.             if batch_idx % log_interval == 0:
  88.                 print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}'.format(
  89.                     epoch + 1, num_epochs, batch_idx, len(train_loader), loss.item()))
  90.             # 保存最佳模型
  91.             if loss.item()<loss_min:
  92.                 loss_min=loss.item()
  93.                 torch.save(model, 'model.pth')
  94.  
  95.     # 模型评估
  96.     with torch.no_grad():
  97.         model.eval()
  98.         correct = 0
  99.         total = 0
  100.         for data, target in test_loader:
  101.             outputs = model(data)
  102.             _, predicted = torch.max(outputs.data, 1)
  103.             total += target.size(0)
  104.             correct += (predicted == target).sum().item()
  105.  
  106.         accuracy = correct / total
  107.         print('Test Accuracy: {:.2%}'.format(accuracy))

 3.模型保存

  1.  # 保存最佳模型
  2.             if loss.item()<loss_min:
  3.                 loss_min=loss.item()
  4.                 torch.save(model, 'model.pth')

4.训练结果 

5.LSTM模型测试（test.py) 

  1. # -*- coding: utf-8 -*-
  2. # @Time : 2023/11/15 15:53
  3. # @Author :Muzi
  4. # @File : test.py.py
  5. # @Software: PyCharm
  6. import torch
  7. import jieba
  8. from torch import nn
  9. from gensim.models import Word2Vec
  10. import numpy as np
  11.  
  12. class LSTMModel(nn.Module):
  13.     def __init__(self, input_size, hidden_size, output_size):
  14.         super(LSTMModel, self).__init__()
  15.         self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
  16.         self.fc = nn.Linear(hidden_size, output_size)
  17.  
  18.     def forward(self, x):
  19.         lstm_out, _ = self.lstm(x)
  20.         output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
  21.         return output
  22.  
  23. # 数据读取
  24. def load_txt(path):
  25.     with open(path,'r',encoding='utf-8') as f:
  26.         data=[[line.strip()] for line in f.readlines()]
  27.         return data
  28.  
  29. #去停用词
  30. def drop_stopword(datas):
  31.     # 假设你有一个函数用于预处理文本数据
  32.     with open('./hit_stopwords.txt', 'r', encoding='UTF8') as f:
  33.         stop_words = [word.strip() for word in f.readlines()]
  34.     datas=[x for x in datas if x not in stop_words]
  35.     return datas
  36.  
  37. def preprocess_text(text):
  38.     text=list(jieba.cut(text))
  39.     text=drop_stopword(text)
  40.     return text
  41.  
  42. # 将文本转换为Word2Vec向量表示
  43. def text_to_vector(text):
  44.     train_x = load_txt('train.txt')
  45.     test_x = load_txt('test.txt')
  46.     train = train_x + test_x
  47.     X_all = [i for x in train for i in x]
  48.     # 训练Word2Vec模型
  49.     word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
  50.     vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
  51.     return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
  52.  
  53. if __name__ == '__main__':
  54.     # input_text = "这个车完全就是垃圾,又热又耗油"
  55.     input_text = "这个车我开了好几年，还是不错的"
  56.     label = {1: "正面情绪", 0: "负面情绪"}
  57.     model = torch.load('model.pth')
  58.     # 预处理输入数据
  59.     input_data = preprocess_text(input_text)
  60.     # 确保输入词向量与模型维度和数据类型相同
  61.     input_data=[[text_to_vector(input_data)]]
  62.     input_arry= np.array(input_data, dtype=np.float32)
  63.     input_tensor = torch.Tensor(input_arry)
  64.     # 将输入数据传入模型
  65.     with torch.no_grad():
  66.         output = model(input_tensor)
  67.     predicted_class = label[torch.argmax(output).item()]
  68.     print(f"predicted_text:{input_text}")
  69.     print(f"模型预测的类别: {predicted_class}")
  70.

 1.测试结果：

2.测试结果：

6.完整代码展示：

1.data_set.py

  1. import pandas as pd
  2. import jieba
  3.  
  4. # 数据读取
  5. def load_tsv(file_path):
  6.     data = pd.read_csv(file_path, sep='\t')
  7.     data_x = data.iloc[:, -1]
  8.     data_y = data.iloc[:, 1]
  9.     return data_x, data_y
  10.  
  11. with open('./hit_stopwords.txt','r',encoding='UTF8') as f:
  12.     stop_words=[word.strip() for word in f.readlines()]
  13.     print('Successfully')
  14. def drop_stopword(datas):
  15.     for data in datas:
  16.         for word in data:
  17.             if word in stop_words:
  18.                 data.remove(word)
  19.     return datas
  20.  
  21. def save_data(datax,path):
  22.     with open(path, 'w', encoding="UTF8") as f:
  23.         for lines in datax:
  24.             for i, line in enumerate(lines):
  25.                 f.write(str(line))
  26.                 # 如果不是最后一行，就添加一个逗号
  27.                 if i != len(lines) - 1:
  28.                     f.write(',')
  29.             f.write('\n')
  30.  
  31. if __name__ == '__main':
  32.     train_x, train_y = load_tsv("./data/train.tsv")
  33.     test_x, test_y = load_tsv("./data/test.tsv")
  34.     train_x = [list(jieba.cut(x)) for x in train_x]
  35.     test_x = [list(jieba.cut(x)) for x in test_x]
  36.     train_x=drop_stopword(train_x)
  37.     test_x=drop_stopword(test_x)
  38.     save_data(train_x,'./train.txt')
  39.     save_data(test_x,'./test.txt')
  40.     print('Successfully')

2.mian.py

  1. import pandas as pd
  2. import torch
  3. from torch import nn
  4. import jieba
  5. from gensim.models import Word2Vec
  6. import numpy as np
  7. from data_set import load_tsv
  8. from torch.utils.data import DataLoader, TensorDataset
  9.  
  10.  
  11. # 数据读取
  12. def load_txt(path):
  13.     with open(path,'r',encoding='utf-8') as f:
  14.         data=[[line.strip()] for line in f.readlines()]
  15.         return data
  16.  
  17. train_x=load_txt('train.txt')
  18. test_x=load_txt('test.txt')
  19. train=train_x+test_x
  20. X_all=[i for x in train for i in x]
  21.  
  22. _, train_y = load_tsv("./data/train.tsv")
  23. _, test_y = load_tsv("./data/test.tsv")
  24. # 训练Word2Vec模型
  25. word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
  26.  
  27. # 将文本转换为Word2Vec向量表示
  28. def text_to_vector(text):
  29.     vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
  30.     return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
  31.  
  32. X_train_w2v = [[text_to_vector(text)] for line in train_x for text in line]
  33. X_test_w2v = [[text_to_vector(text)] for line in test_x for text in line]
  34.  
  35. # 将词向量转换为PyTorch张量
  36. X_train_array = np.array(X_train_w2v, dtype=np.float32)
  37. X_train_tensor = torch.Tensor(X_train_array)
  38. X_test_array = np.array(X_test_w2v, dtype=np.float32)
  39. X_test_tensor = torch.Tensor(X_test_array)
  40. #使用DataLoader打包文件
  41. train_dataset = TensorDataset(X_train_tensor, torch.LongTensor(train_y))
  42. train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
  43. test_dataset = TensorDataset(X_test_tensor,torch.LongTensor(test_y))
  44. test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)
  45. # 定义LSTM模型
  46. class LSTMModel(nn.Module):
  47.     def __init__(self, input_size, hidden_size, output_size):
  48.         super(LSTMModel, self).__init__()
  49.         self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
  50.         self.fc = nn.Linear(hidden_size, output_size)
  51.  
  52.     def forward(self, x):
  53.         lstm_out, _ = self.lstm(x)
  54.         output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
  55.         return output
  56.  
  57. # 定义模型
  58. input_size = word2vec_model.vector_size
  59. hidden_size = 50  # 你可以根据需要调整隐藏层大小
  60. output_size = 2  # 输出的大小，根据你的任务而定
  61.  
  62. model = LSTMModel(input_size, hidden_size, output_size)
  63. # 定义损失函数和优化器
  64. criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
  65. optimizer = torch.optim.Adam(model.parameters(), lr=0.0002)
  66.  
  67. if __name__ == "__main__":
  68.     # 训练模型
  69.     num_epochs = 10
  70.     log_interval = 100  # 每隔100个批次输出一次日志
  71.     loss_min=100
  72.     for epoch in range(num_epochs):
  73.         model.train()
  74.         for batch_idx, (data, target) in enumerate(train_loader):
  75.             outputs = model(data)
  76.             loss = criterion(outputs, target)
  77.  
  78.             optimizer.zero_grad()
  79.             loss.backward()
  80.             optimizer.step()
  81.  
  82.             if batch_idx % log_interval == 0:
  83.                 print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}'.format(
  84.                     epoch + 1, num_epochs, batch_idx, len(train_loader), loss.item()))
  85.             # 保存最佳模型
  86.             if loss.item()<loss_min:
  87.                 loss_min=loss.item()
  88.                 torch.save(model, 'model.pth')
  89.  
  90.     # 模型评估
  91.     with torch.no_grad():
  92.         model.eval()
  93.         correct = 0
  94.         total = 0
  95.         for data, target in test_loader:
  96.             outputs = model(data)
  97.             _, predicted = torch.max(outputs.data, 1)
  98.             total += target.size(0)
  99.             correct += (predicted == target).sum().item()
  100.  
  101.         accuracy = correct / total
  102.         print('Test Accuracy: {:.2%}'.format(accuracy))

3.test.py 

  1. import torch
  2. import jieba
  3. from torch import nn
  4. from gensim.models import Word2Vec
  5. import numpy as np
  6.  
  7. class LSTMModel(nn.Module):
  8.     def __init__(self, input_size, hidden_size, output_size):
  9.         super(LSTMModel, self).__init__()
  10.         self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
  11.         self.fc = nn.Linear(hidden_size, output_size)
  12.  
  13.     def forward(self, x):
  14.         lstm_out, _ = self.lstm(x)
  15.         output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
  16.         return output
  17.  
  18. # 数据读取
  19. def load_txt(path):
  20.     with open(path,'r',encoding='utf-8') as f:
  21.         data=[[line.strip()] for line in f.readlines()]
  22.         return data
  23.  
  24. #去停用词
  25. def drop_stopword(datas):
  26.     # 假设你有一个函数用于预处理文本数据
  27.     with open('./hit_stopwords.txt', 'r', encoding='UTF8') as f:
  28.         stop_words = [word.strip() for word in f.readlines()]
  29.     datas=[x for x in datas if x not in stop_words]
  30.     return datas
  31.  
  32. def preprocess_text(text):
  33.     text=list(jieba.cut(text))
  34.     text=drop_stopword(text)
  35.     return text
  36.  
  37. # 将文本转换为Word2Vec向量表示
  38. def text_to_vector(text):
  39.     train_x = load_txt('train.txt')
  40.     test_x = load_txt('test.txt')
  41.     train = train_x + test_x
  42.     X_all = [i for x in train for i in x]
  43.     # 训练Word2Vec模型
  44.     word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
  45.     vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
  46.     return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
  47.  
  48. if __name__ == '__main__':
  49.     input_text = "这个车完全就是垃圾,又热又耗油"
  50.     # input_text = "这个车我开了好几年，还是不错的"
  51.     label = {1: "正面情绪", 0: "负面情绪"}
  52.     model = torch.load('model.pth')
  53.     # 预处理输入数据
  54.     input_data = preprocess_text(input_text)
  55.     # 确保输入词向量与模型维度和数据类型相同
  56.     input_data=[[text_to_vector(input_data)]]
  57.     input_arry= np.array(input_data, dtype=np.float32)
  58.     input_tensor = torch.Tensor(input_arry)
  59.     # 将输入数据传入模型
  60.     with torch.no_grad():
  61.         output = model(input_tensor)
  62.     # 这里只一个简单的示例
  63.     predicted_class = label[torch.argmax(output).item()]
  64.     print(f"predicted_text:{input_text}")
  65.     print(f"模型预测的类别: {predicted_class}")

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