glove文本分类中文微博评论python代码

以下是使用Python实现基于GloVe的中文微博评论文本分类的示例代码，其中使用了gensim库和jieba库：

import jieba
import numpy as np
from gensim.models import KeyedVectors
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM

# 准备中文微博评论数据集，这里用的是一个文本文件
data = []
labels = []
with open('weibo_comments.txt', 'r', encoding='utf-8') as f:
    for line in f:
        # 每一行数据包括评论和标签，用空格分隔
        label, comment = line.strip().split(' ')
        data.append(comment)
        labels.append(label)

# 对标签进行编码
label_encoder = LabelEncoder()
labels = label_encoder.fit_transform(labels)

# 对微博评论进行分词
sentences = []
for comment in data:
    words = list(jieba.cut(comment))
    sentences.append(words)

# 加载预训练好的GloVe词向量
word_vectors = KeyedVectors.load_word2vec_format('glove.word2vec.txt', binary=False)

# 将分好词的微博评论转换为词向量序列
embedding_size = 100
max_sequence_length = 100
embedding_matrix = np.zeros((len(word_vectors.vocab), embedding_size))
for i, word in enumerate(word_vectors.index2word):
    if i >= len(word_vectors.vocab):
        break
    embedding_vector = word_vectors[word]
    if embedding_vector is not None:
        embedding_matrix[i] = embedding_vector

data_sequences = []
for sentence in sentences:
    sequence = []
    for word in sentence:
        if word in word_vectors.vocab:
            sequence.append(word_vectors.vocab[word].index)
    data_sequences.append(sequence)

# 对词向量序列进行padding，使其长度相同
data_sequences = pad_sequences(data_sequences, maxlen=max_sequence_length)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data_sequences, labels, test_size=0.2, random_state=42)

# 构建模型
model = Sequential()
model.add(Embedding(len(word_vectors.vocab), embedding_size, weights=[embedding_matrix], input_length=max_sequence_length, trainable=False))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
batch_size = 32
epochs = 10
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))

# 评估模型
loss, accuracy = model.evaluate(X_test, y_test)
print('Test loss:', loss)
print('Test accuracy:', accuracy)

在上述代码中，我们使用`jieba`对微博评论进行分词，并使用预训练好的GloVe词向量将分好词的微博评论转换为词向量序列。然后，我们使用Keras构建一个LSTM模型，并使用训练集对模型进行训练。最后，我们使用测试集对模型进行评估。需要注意的是，这里的示例中只是简单地使用了一个文本文件作为数据集，实际应用中需要使用更大的数据集进行训练和测试。