Python中Vocabulary()类与word2vec算法的结合应用

在Python中，Vocabulary()类与word2vec算法可以结合使用来创建一个词汇表，并使用word2vec算法训练模型以得到每个单词的嵌入向量。这个词汇表可以用于许多自然语言处理任务，例如文本分类、命名实体识别和情感分析等。

下面是一个使用Vocabulary()类和word2vec算法的示例，以将电影评论分类为正面或负面：

1. 导入必要的库

from gensim.models import Word2Vec
from gensim.models.callbacks import CallbackAny2Vec
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

2. 定义一个回调类，用于跟踪Word2Vec模型的训练进度

class EpochLogger(CallbackAny2Vec):
    def __init__(self):
        self.epoch = 0
    
    def on_epoch_end(self, model):
        self.epoch += 1
        print("Epoch #{} was trained.".format(self.epoch))

3. 准备数据

# 电影评论数据集，包含正面和负面评论
reviews = [
    "I loved this movie! It was amazing.",
    "The acting was terrible in this film.",
    "This film had a great storyline.",
    "The plot was confusing and hard to follow.",
    "The cinematography was beautiful.",
    "The dialogue was cheesy and unrealistic."
]

# 评论对应的标签，1表示正面，0表示负面
labels = [1, 0, 1, 0, 1, 0]

4. 将评论文本转换为词袋表示

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)

5. 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)

6. 使用Vocabulary()类构建词汇表

vocab = Vocabulary()
vocab.build_from_corpus(reviews)

7. 使用word2vec算法训练模型

model = Word2Vec(
    sentences=vocab.sentences,
    vector_size=100,
    window=5,
    min_count=1,
    workers=4,
    callbacks=[EpochLogger()]
)

8. 构建训练数据集的嵌入向量表示

X_train_embedded = []
for review in X_train:
    embedded_review = []
    for word in review.split():
        if word in model.wv:
            embedded_review.append(model.wv[word])
    if len(embedded_review) > 0:
        X_train_embedded.append(np.mean(embedded_review, axis=0))
    else:
        X_train_embedded.append(np.zeros(model.vector_size))
X_train_embedded = np.array(X_train_embedded)

9. 训练逻辑回归模型

classifier = LogisticRegression()
classifier.fit(X_train_embedded, y_train)

10. 构建测试数据集的嵌入向量表示并进行预测

X_test_embedded = []
for review in X_test:
    embedded_review = []
    for word in review.split():
        if word in model.wv:
            embedded_review.append(model.wv[word])
    if len(embedded_review) > 0:
        X_test_embedded.append(np.mean(embedded_review, axis=0))
    else:
        X_test_embedded.append(np.zeros(model.vector_size))
X_test_embedded = np.array(X_test_embedded)

y_pred = classifier.predict(X_test_embedded)

11. 评估模型性能

accuracy = (y_pred == y_test).mean()
print("Accuracy: {}".format(accuracy))

通过上述步骤，我们使用Vocabulary()类构建了一个词汇表，并使用word2vec算法训练了一个模型，然后将评论转换为嵌入向量表示，并使用逻辑回归模型对评论进行分类。最后，我们评估了模型的准确性。

这个示例演示了Vocabulary()类与word2vec算法的结合应用。该方法可以扩展到其他自然语言处理任务，只需根据具体任务调整和优化模型和数据处理流程。