对于卷积神经网络识别恶意评论写一段代码

以下是一段基于卷积神经网络的代码，用于识别恶意评论：

import pandas as pd
import numpy as np
import re
import nltk
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Conv1D, MaxPool1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.models import Model
from keras.callbacks import ModelCheckpoint

# 导入数据
data = pd.read_csv("comment_data.csv")

# 对评论进行清洗和预处理
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
def clean_text(text):
    # 将文本中的标点符号、数字、特殊符号等内容去除
    text = re.sub('[^a-zA-Z\s]', '', text)
    # 将所有字母转换成小写
    text = text.lower()
    # 去除停用词
    text = ' '.join([word for word in text.split() if word not in stop_words])
    return text

data['clean_text'] = data['text'].apply(clean_text)

# 构建词汇表，将文本转换为数字序列
tokenizer = Tokenizer(num_words=5000, split=' ')
tokenizer.fit_on_texts(data['clean_text'].values)
X = tokenizer.texts_to_sequences(data['clean_text'].values)
X = pad_sequences(X)

# 构建卷积神经网络模型
input_shape = X.shape[1]
input_layer = Input(shape=(input_shape,))
embedding_layer = Embedding(5000, 128, input_length=input_shape)(input_layer)
conv_layer = Conv1D(filters=128, kernel_size=3, activation='relu')(embedding_layer)
pool_layer = MaxPool1D(pool_size=2)(conv_layer)
flatten_layer = Flatten()(pool_layer)
hidden_layer = Dense(50, activation='relu')(flatten_layer)
output_layer = Dense(1, activation='sigmoid')(hidden_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 将数据分成训练集和测试集
Y = data['is_offensive'].values
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.20, random_state = 42)

# 训练模型
checkpoint = ModelCheckpoint("model.h5", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
model.fit(X_train, Y_train, epochs=10, batch_size=64, validation_data=(X_test, Y_test), callbacks=callbacks_list)

# 使用模型进行预测
def predict(text):
    text = clean_text(text)
    sequence = tokenizer.texts_to_sequences([text])
    sequence = pad_sequences(sequence, maxlen=input_shape)
    prediction = model.predict(sequence)
    if prediction[0][0] > 0.5:
        return "恶意评论"
    else:
        return "正常评论"

# 测试模型
print(predict("这篇文章非常棒"))
print(predict("这个游戏真的很烂，一点也不好玩"))

注意：该代码仅供参考，具体实现需要根据具体数据集和任务进行调整。