一  前言

情感分析（Sentiment Analysis），也称为情感分类，属于自然语言处理（Natural Language Processing，NLP）领域的一个分支任务，随着互联网的发展而兴起。多数情况下该任务分析一个文本所呈现的信息是正面、负面或者中性，也有一些研究会区分得更细，例如在正负极性中再进行分级，区分不同情感强度.

文本情感分析（Sentiment Analysis）是自然语言处理（NLP）方法中常见的应用，也是一个有趣的基本任务，尤其是以提炼文本情绪内容为目的的分类。它是对带有情感色彩的主观性文本进行分析、处理、归纳和推理的过程。
情感分析中的情感极性（倾向）分析。所谓情感极性分析，指的是对文本进行褒义、贬义、中性的判断。在大多应用场景下，只分为两类。例如对于“喜爱”和“厌恶”这两个词，就属于不同的情感倾向。

本文将采用LSTM模型，训练一个能够识别文本postive, negative情感的分类器。

 

RNN网络因为使用了单词的序列信息，所以准确率要比前向传递神经网络要高。
 

网络结构：

 

首先，将单词传入 embedding层，之所以使用嵌入层，是因为单词数量太多，使用嵌入式词向量来表示单词更有效率。在这里我们使用word2vec方式来实现，而且特别神奇的是，我们只需要加入嵌入层即可，网络会自主学习嵌入矩阵

参考下图

通过embedding 层, 新的单词表示传入 LSTM cells。这将是一个递归链接网络，所以单词的序列信息会在网络之间传递。最后， LSTM cells连接一个sigmoid output layer 。 使用sigmoid可以预测该文本是 积极的 还是 消极的 情感。输出层只有一个单元节点（使用sigmoid激活）。

只需要关注最后一个sigmoid的输出，损失只计算最后一步的输出和标签的差异。

文件说明：
（1）reviews.txt 是原始文本文件，共25000条，一行是一篇英文电影影评文本
（2）labels.txt 是标签文件，共25000条，一行是一个标签，positive 或者 negative

二   模型训练与预测

1、Data Preprocessing
建任何模型的第一步，永远是数据清洗。 因为使用embedding 层,需要将单词编码成整数。

我们要去除标点符号。 同时，去除不同文本之间有分隔符号 \n，我们先把\n当成分隔符号，分割所有评论。 然后在将所有评论再次连接成为一个大的文本。

import numpy as np

# read data from text files
with open('./data/reviews.txt', 'r') as f:
    reviews = f.read()
with open('./data/labels.txt', 'r') as f:
    labels = f.read()

print(reviews[:1000])
print()
print(labels[:20])

from string import punctuation

# get rid of punctuation
reviews = reviews.lower() # lowercase, standardize
all_text = ''.join([c for c in reviews if c not in punctuation])

# split by new lines and spaces
reviews_split = all_text.split('\n')
all_text = ' '.join(reviews_split)

# create a list of words
words = all_text.split()

2、Encoding the words

embedding lookup要求输入的网络数据是整数。最简单的方法就是创建数据字典：{单词：整数}。然后将评论全部一一对应转换成整数，传入网络。

# feel free to use this import 
from collections import Counter

## Build a dictionary that maps words to integers
counts = Counter(words)
vocab = sorted(counts, key=counts.get, reverse=True)
vocab_to_int = {word: ii for ii, word in enumerate(vocab, 1)}

## use the dict to tokenize each review in reviews_split
## store the tokenized reviews in reviews_ints
reviews_ints = []
for review in reviews_split:
    reviews_ints.append([vocab_to_int[word] for word in review.split()])



# stats about vocabulary
print('Unique words: ', len((vocab_to_int)))  # should ~ 74000+
print()

# print tokens in first review
print('Tokenized review: \n', reviews_ints[:1])

补充enumerate函数用法:
在enumerate函数内写上int整型数字，则以该整型数字作为起始去迭代生成结果。

 

3、Encoding the labels

将标签 “positive” or "negative"转换为数值。

# 1=positive, 0=negative label conversion
labels_split = labels.split('\n')
encoded_labels = np.array([1 if label == 'positive' else 0 for label in labels_split])

# outlier review stats
review_lens = Counter([len(x) for x in reviews_ints])
print("Zero-length reviews: {}".format(review_lens[0]))
print("Maximum review length: {}".format(max(review_lens)))

消除长度为0的行

print('Number of reviews before removing outliers: ', len(reviews_ints))

## remove any reviews/labels with zero length from the reviews_ints list.

# get indices of any reviews with length 0
non_zero_idx = [ii for ii, review in enumerate(reviews_ints) if len(review) != 0]

# remove 0-length reviews and their labels
reviews_ints = [reviews_ints[ii] for ii in non_zero_idx]
encoded_labels = np.array([encoded_labels[ii] for ii in non_zero_idx])

print('Number of reviews after removing outliers: ', len(reviews_ints))

 

4、Padding sequences

将所以句子统一长度为200个单词：
1、评论长度小于200的，我们对其左边填充0
2、对于大于200的，我们只截取其前200个单词

#选择每个句子长为200
seq_len = 200
from tensorflow.contrib.keras import preprocessing
features = np.zeros((len(reviews_ints),seq_len),dtype=int)
#将reviews_ints值逐行 赋值给features
features = preprocessing.sequence.pad_sequences(reviews_ints,200)
features.shape

或者

def pad_features(reviews_ints, seq_length):
    ''' Return features of review_ints, where each review is padded with 0's 
        or truncated to the input seq_length.
    '''
    
    # getting the correct rows x cols shape
    features = np.zeros((len(reviews_ints), seq_length), dtype=int)

    # for each review, I grab that review and 
    for i, row in enumerate(reviews_ints):
        features[i, -len(row):] = np.array(row)[:seq_length]
    
    return features



# Test your implementation!

seq_length = 200

features = pad_features(reviews_ints, seq_length=seq_length)

## test statements - do not change - ##
assert len(features)==len(reviews_ints), "Your features should have as many rows as reviews."
assert len(features[0])==seq_length, "Each feature row should contain seq_length values."

# print first 10 values of the first 30 batches 
print(features[:30,:10])

 

5、Training, Test划分

split_frac = 0.8

## split data into training, validation, and test data (features and labels, x and y)

split_idx = int(len(features)*split_frac)
train_x, remaining_x = features[:split_idx], features[split_idx:]
train_y, remaining_y = encoded_labels[:split_idx], encoded_labels[split_idx:]

test_idx = int(len(remaining_x)*0.5)
val_x, test_x = remaining_x[:test_idx], remaining_x[test_idx:]
val_y, test_y = remaining_y[:test_idx], remaining_y[test_idx:]

## print out the shapes of your resultant feature data
print("\t\t\tFeature Shapes:")
print("Train set: \t\t{}".format(train_x.shape), 
      "\nValidation set: \t{}".format(val_x.shape),
      "\nTest set: \t\t{}".format(test_x.shape))

或

from sklearn.model_selection import ShuffleSplit
ss = ShuffleSplit(n_splits=1,test_size=0.2,random_state=0)
for train_index,test_index in ss.split(np.array(reviews_ints)):
    train_x = features[train_index]
    train_y = labels[train_index]
    test_x = features[test_index]
    test_y = labels[test_index]

print("\t\t\tFeature Shapes:")
print("Train set: \t\t{}".format(train_x.shape), 
      "\nTrain_Y set: \t{}".format(train_y.shape),
      "\nTest set: \t\t{}".format(test_x.shape))

 

6. DataLoaders and Batching

import torch
from torch.utils.data import TensorDataset, DataLoader

# create Tensor datasets
train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))
valid_data = TensorDataset(torch.from_numpy(val_x), torch.from_numpy(val_y))
test_data = TensorDataset(torch.from_numpy(test_x), torch.from_numpy(test_y))

# dataloaders
batch_size = 50

# make sure the SHUFFLE your training data
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size)
test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)

 

# obtain one batch of training data
dataiter = iter(train_loader)
sample_x, sample_y = dataiter.next()

print('Sample input size: ', sample_x.size()) # batch_size, seq_length
print('Sample input: \n', sample_x)
print()
print('Sample label size: ', sample_y.size()) # batch_size
print('Sample label: \n', sample_y)

 

7. 双向LSTM模型

1. 判断是否有GPU

# First checking if GPU is available
train_on_gpu=torch.cuda.is_available()

if(train_on_gpu):
    print('Training on GPU.')
else:
    print('No GPU available, training on CPU.')

import torch.nn as nn

class SentimentRNN(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, bidirectional=True, drop_prob=0.5):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentRNN, self).__init__()

        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim
        self.bidirectional = bidirectional
        
        # embedding and LSTM layers
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, 
                            dropout=drop_prob, batch_first=True,
                            bidirectional=bidirectional)
        
        # dropout layer
        self.dropout = nn.Dropout(0.3)
        
        # linear and sigmoid layers
        if bidirectional:
          self.fc = nn.Linear(hidden_dim*2, output_size)
        else:
          self.fc = nn.Linear(hidden_dim, output_size)
          
        self.sig = nn.Sigmoid()
        

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size = x.size(0)

        # embeddings and lstm_out
        x = x.long()
        embeds = self.embedding(x)
        lstm_out, hidden = self.lstm(embeds, hidden)
        
#         if bidirectional:
#           lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim*2)
#         else:
#           lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)
       
        # dropout and fully-connected layer
        out = self.dropout(lstm_out)
        out = self.fc(out)
        # sigmoid function
        sig_out = self.sig(out)
        
        # reshape to be batch_size first
        sig_out = sig_out.view(batch_size, -1)
        sig_out = sig_out[:, -1] # get last batch of labels
        
        # return last sigmoid output and hidden state
        return sig_out, hidden
    
    
    def init_hidden(self, batch_size):
        ''' Initializes hidden state '''
        # Create two new tensors with sizes n_layers x batch_size x hidden_dim,
        # initialized to zero, for hidden state and cell state of LSTM
        weight = next(self.parameters()).data
        
        number = 1
        if self.bidirectional:
           number = 2
        
        if (train_on_gpu):
            hidden = (weight.new(self.n_layers*number, batch_size, self.hidden_dim).zero_().cuda(),
                      weight.new(self.n_layers*number, batch_size, self.hidden_dim).zero_().cuda()
                     )
        else:
            hidden = (weight.new(self.n_layers*number, batch_size, self.hidden_dim).zero_(),
                      weight.new(self.n_layers*number, batch_size, self.hidden_dim).zero_()
                     )
        
        return hidden

是否使用双向LSTM（在测试集上效果更好一些）

# Instantiate the model w/ hyperparams
vocab_size = len(vocab_to_int)+1 # +1 for the 0 padding + our word tokens
output_size = 1
embedding_dim = 400
hidden_dim = 256
n_layers = 2
bidirectional = False  #这里为True，为双向LSTM

net = SentimentRNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers, bidirectional)

print(net)

 

8 Train

# loss and optimization functions
lr=0.001

criterion = nn.BCELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)


# training params

epochs = 4 # 3-4 is approx where I noticed the validation loss stop decreasing

print_every = 100
clip=5 # gradient clipping

# move model to GPU, if available
if(train_on_gpu):
    net.cuda()

net.train()
# train for some number of epochs
for e in range(epochs):
    # initialize hidden state
    h = net.init_hidden(batch_size)
    counter = 0

    # batch loop
    for inputs, labels in train_loader:
        counter += 1

        if(train_on_gpu):
            inputs, labels = inputs.cuda(), labels.cuda()

        # Creating new variables for the hidden state, otherwise
        # we'd backprop through the entire training history
        h = tuple([each.data for each in h])
        # zero accumulated gradients
        net.zero_grad()

        # get the output from the model
        output, h = net(inputs, h)

        # calculate the loss and perform backprop
        loss = criterion(output.squeeze(), labels.float())
        loss.backward()
        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
        nn.utils.clip_grad_norm_(net.parameters(), clip)
        optimizer.step()

        # loss stats
        if counter % print_every == 0:
            # Get validation loss
            val_h = net.init_hidden(batch_size)
            val_losses = []
            net.eval()
            for inputs, labels in valid_loader:

                # Creating new variables for the hidden state, otherwise
                # we'd backprop through the entire training history
                val_h = tuple([each.data for each in val_h])

                if(train_on_gpu):
                    inputs, labels = inputs.cuda(), labels.cuda()

                output, val_h = net(inputs, val_h)
                val_loss = criterion(output.squeeze(), labels.float())

                val_losses.append(val_loss.item())

            net.train()
            print("Epoch: {}/{}...".format(e+1, epochs),
                  "Step: {}...".format(counter),
                  "Loss: {:.6f}...".format(loss.item()),
                  "Val Loss: {:.6f}".format(np.mean(val_losses)))

 

 

9 Test

# Get test data loss and accuracy

test_losses = [] # track loss
num_correct = 0

# init hidden state
h = net.init_hidden(batch_size)

net.eval()
# iterate over test data
for inputs, labels in test_loader:

    # Creating new variables for the hidden state, otherwise
    # we'd backprop through the entire training history
    h = tuple([each.data for each in h])

    if(train_on_gpu):
        inputs, labels = inputs.cuda(), labels.cuda()
    
    # get predicted outputs
    output, h = net(inputs, h)
    
    # calculate loss
    test_loss = criterion(output.squeeze(), labels.float())
    test_losses.append(test_loss.item())
    
    # convert output probabilities to predicted class (0 or 1)
    pred = torch.round(output.squeeze())  # rounds to the nearest integer
    
    # compare predictions to true label
    correct_tensor = pred.eq(labels.float().view_as(pred))
    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())
    num_correct += np.sum(correct)


# -- stats! -- ##
# avg test loss
print("Test loss: {:.3f}".format(np.mean(test_losses)))

# accuracy over all test data
test_acc = num_correct/len(test_loader.dataset)
print("Test accuracy: {:.3f}".format(test_acc))

 

 三.   模型Inference

# negative test review
test_review_neg = 'The worst movie I have seen; acting was terrible and I want my money back. This movie had bad acting and the dialogue was slow.'

from string import punctuation

def tokenize_review(test_review):
    test_review = test_review.lower() # lowercase
    # get rid of punctuation
    test_text = ''.join([c for c in test_review if c not in punctuation])

    # splitting by spaces
    test_words = test_text.split()

    # tokens
    test_ints = []
    test_ints.append([vocab_to_int[word] for word in test_words])

    return test_ints

# test code and generate tokenized review
test_ints = tokenize_review(test_review_neg)
print(test_ints)


# test sequence padding
seq_length=200
features = pad_features(test_ints, seq_length)
print(features)

# test conversion to tensor and pass into your model
feature_tensor = torch.from_numpy(features)
print(feature_tensor.size())

 

def predict(net, test_review, sequence_length=200):
    
    net.eval()
    
    # tokenize review
    test_ints = tokenize_review(test_review)
    
    # pad tokenized sequence
    seq_length=sequence_length
    features = pad_features(test_ints, seq_length)
    
    # convert to tensor to pass into your model
    feature_tensor = torch.from_numpy(features)
    
    batch_size = feature_tensor.size(0)
    
    # initialize hidden state
    h = net.init_hidden(batch_size)
    
    if(train_on_gpu):
        feature_tensor = feature_tensor.cuda()
    
    # get the output from the model
    output, h = net(feature_tensor, h)
    
    # convert output probabilities to predicted class (0 or 1)
    pred = torch.round(output.squeeze()) 
    # printing output value, before rounding
    print('Prediction value, pre-rounding: {:.6f}'.format(output.item()))
    
    # print custom response
    if(pred.item()==1):
        print("Positive review detected!")
    else:
        print("Negative review detected.")
        

 

# positive test review
test_review_pos = 'This movie had the best acting and the dialogue was so good. I loved it.'

# call function
seq_length=200 # good to use the length that was trained on

predict(net, test_review_neg, seq_length)