15.1. 情感分析及数据集
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随着在线社交媒体和评论平台的快速发展,大量评论的数据被记录下来。这些数据具有支持决策过程的巨大潜力。 情感分析(sentiment analysis)研究人们在文本中 (如产品评论、博客评论和论坛讨论等)“隐藏”的情绪。 它在广泛应用于政治(如公众对政策的情绪分析)、 金融(如市场情绪分析)和营销(如产品研究和品牌管理)等领域。

由于情感可以被分类为离散的极性或尺度(例如,积极的和消极的),我们可以将情感分析看作一项文本分类任务,它将可变长度的文本序列转换为固定长度的文本类别。在本章中,我们将使用斯坦福大学的大型电影评论数据集(large movie review dataset)进行情感分析。它由一个训练集和一个测试集组成,其中包含从IMDb下载的25000个电影评论。在这两个数据集中,“积极”和“消极”标签的数量相同,表示不同的情感极性。

import os
from mxnet import np, npx
from d2l import mxnet as d2l

npx.set_np()
import os
import torch
from torch import nn
from d2l import torch as d2l

15.1.1. 读取数据集

首先,下载并提取路径../data/aclImdb中的IMDb评论数据集。

#@save
d2l.DATA_HUB['aclImdb'] = (
    'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz',
    '01ada507287d82875905620988597833ad4e0903')

data_dir = d2l.download_extract('aclImdb', 'aclImdb')
Downloading ../data/aclImdb_v1.tar.gz from http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz...
#@save
d2l.DATA_HUB['aclImdb'] = (
    'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz',
    '01ada507287d82875905620988597833ad4e0903')

data_dir = d2l.download_extract('aclImdb', 'aclImdb')

接下来,读取训练和测试数据集。每个样本都是一个评论及其标签:1表示“积极”,0表示“消极”。

#@save
def read_imdb(data_dir, is_train):
    """读取IMDb评论数据集文本序列和标签"""
    data, labels = [], []
    for label in ('pos', 'neg'):
        folder_name = os.path.join(data_dir, 'train' if is_train else 'test',
                                   label)
        for file in os.listdir(folder_name):
            with open(os.path.join(folder_name, file), 'rb') as f:
                review = f.read().decode('utf-8').replace('\n', '')
                data.append(review)
                labels.append(1 if label == 'pos' else 0)
    return data, labels

train_data = read_imdb(data_dir, is_train=True)
print('训练集数目:', len(train_data[0]))
for x, y in zip(train_data[0][:3], train_data[1][:3]):
    print('标签:', y, 'review:', x[0:60])
训练集数目: 25000
标签: 1 review: Henry Hathaway was daring, as well as enthusiastic, for his
标签: 1 review: An unassuming, subtle and lean film, "The Man in the White S
标签: 1 review: Eddie Murphy really made me laugh my ass off on this HBO sta
#@save
def read_imdb(data_dir, is_train):
    """读取IMDb评论数据集文本序列和标签"""
    data, labels = [], []
    for label in ('pos', 'neg'):
        folder_name = os.path.join(data_dir, 'train' if is_train else 'test',
                                   label)
        for file in os.listdir(folder_name):
            with open(os.path.join(folder_name, file), 'rb') as f:
                review = f.read().decode('utf-8').replace('\n', '')
                data.append(review)
                labels.append(1 if label == 'pos' else 0)
    return data, labels

train_data = read_imdb(data_dir, is_train=True)
print('训练集数目:', len(train_data[0]))
for x, y in zip(train_data[0][:3], train_data[1][:3]):
    print('标签:', y, 'review:', x[0:60])
训练集数目: 25000
标签: 1 review: Henry Hathaway was daring, as well as enthusiastic, for his
标签: 1 review: An unassuming, subtle and lean film, "The Man in the White S
标签: 1 review: Eddie Murphy really made me laugh my ass off on this HBO sta

15.1.2. 预处理数据集

将每个单词作为一个词元,过滤掉出现不到5次的单词,我们从训练数据集中创建一个词表。

train_tokens = d2l.tokenize(train_data[0], token='word')
vocab = d2l.Vocab(train_tokens, min_freq=5, reserved_tokens=['<pad>'])
train_tokens = d2l.tokenize(train_data[0], token='word')
vocab = d2l.Vocab(train_tokens, min_freq=5, reserved_tokens=['<pad>'])

在词元化之后,让我们绘制评论词元长度的直方图。

d2l.set_figsize()
d2l.plt.xlabel('# tokens per review')
d2l.plt.ylabel('count')
d2l.plt.hist([len(line) for line in train_tokens], bins=range(0, 1000, 50));
../_images/output_sentiment-analysis-and-dataset_70179c_39_0.svg
d2l.set_figsize()
d2l.plt.xlabel('# tokens per review')
d2l.plt.ylabel('count')
d2l.plt.hist([len(line) for line in train_tokens], bins=range(0, 1000, 50));
../_images/output_sentiment-analysis-and-dataset_70179c_42_0.svg

正如我们所料,评论的长度各不相同。为了每次处理一小批量这样的评论,我们通过截断和填充将每个评论的长度设置为500。这类似于 9.5节中对机器翻译数据集的预处理步骤。

num_steps = 500  # 序列长度
train_features = np.array([d2l.truncate_pad(
    vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
print(train_features.shape)
(25000, 500)
num_steps = 500  # 序列长度
train_features = torch.tensor([d2l.truncate_pad(
    vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
print(train_features.shape)
torch.Size([25000, 500])

15.1.3. 创建数据迭代器

现在我们可以创建数据迭代器了。在每次迭代中,都会返回一小批量样本。

train_iter = d2l.load_array((train_features, train_data[1]), 64)

for X, y in train_iter:
    print('X:', X.shape, ', y:', y.shape)
    break
print('小批量数目:', len(train_iter))
X: (64, 500) , y: (64,)
小批量数目: 391
train_iter = d2l.load_array((train_features,
    torch.tensor(train_data[1])), 64)

for X, y in train_iter:
    print('X:', X.shape, ', y:', y.shape)
    break
print('小批量数目:', len(train_iter))
X: torch.Size([64, 500]) , y: torch.Size([64])
小批量数目: 391

15.1.4. 整合代码

最后,我们将上述步骤封装到load_data_imdb函数中。它返回训练和测试数据迭代器以及IMDb评论数据集的词表。

#@save
def load_data_imdb(batch_size, num_steps=500):
    """返回数据迭代器和IMDb评论数据集的词表"""
    data_dir = d2l.download_extract('aclImdb', 'aclImdb')
    train_data = read_imdb(data_dir, True)
    test_data = read_imdb(data_dir, False)
    train_tokens = d2l.tokenize(train_data[0], token='word')
    test_tokens = d2l.tokenize(test_data[0], token='word')
    vocab = d2l.Vocab(train_tokens, min_freq=5)
    train_features = np.array([d2l.truncate_pad(
        vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
    test_features = np.array([d2l.truncate_pad(
        vocab[line], num_steps, vocab['<pad>']) for line in test_tokens])
    train_iter = d2l.load_array((train_features, train_data[1]), batch_size)
    test_iter = d2l.load_array((test_features, test_data[1]), batch_size,
                               is_train=False)
    return train_iter, test_iter, vocab
#@save
def load_data_imdb(batch_size, num_steps=500):
    """返回数据迭代器和IMDb评论数据集的词表"""
    data_dir = d2l.download_extract('aclImdb', 'aclImdb')
    train_data = read_imdb(data_dir, True)
    test_data = read_imdb(data_dir, False)
    train_tokens = d2l.tokenize(train_data[0], token='word')
    test_tokens = d2l.tokenize(test_data[0], token='word')
    vocab = d2l.Vocab(train_tokens, min_freq=5)
    train_features = torch.tensor([d2l.truncate_pad(
        vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
    test_features = torch.tensor([d2l.truncate_pad(
        vocab[line], num_steps, vocab['<pad>']) for line in test_tokens])
    train_iter = d2l.load_array((train_features, torch.tensor(train_data[1])),
                                batch_size)
    test_iter = d2l.load_array((test_features, torch.tensor(test_data[1])),
                               batch_size,
                               is_train=False)
    return train_iter, test_iter, vocab

15.1.5. 小结

  • 情感分析研究人们在文本中的情感,这被认为是一个文本分类问题,它将可变长度的文本序列进行转换转换为固定长度的文本类别。

  • 经过预处理后,我们可以使用词表将IMDb评论数据集加载到数据迭代器中。

15.1.6. 练习

  1. 我们可以修改本节中的哪些超参数来加速训练情感分析模型?

  2. 你能实现一个函数来将Amazon reviews的数据集加载到数据迭代器中进行情感分析吗?