cnn对文本进行情感极性分析，告诉我代码，情感词典我有

好的，首先需要导入必要的库，包括`numpy`、`pandas`、`keras`等，代码如下：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, Conv1D, MaxPooling1D, Dropout
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split

然后，我们将情感词典读入并进行预处理，包括去除停用词、分词等操作，代码如下：

# 读入情感词典
sentiment_dict = pd.read_excel('情感词典.xlsx')

# 去除停用词
stopwords = pd.read_csv('stopwords.txt', header=None, sep='\t')
stopwords = list(stopwords[0])

sentiment_dict = sentiment_dict[~sentiment_dict['word'].isin(stopwords)]

# 分词
import jieba
sentiment_dict['word'] = sentiment_dict['word'].apply(lambda x: ' '.join(jieba.cut(x)))

# 构建情感词典
pos_dict = sentiment_dict[sentiment_dict['positive'] == 1]['word'].tolist()
neg_dict = sentiment_dict[sentiment_dict['negative'] == 1]['word'].tolist()

# 定义函数，判断文本中的情感词并计数
def count_sentiment_words(text):
    pos_words = [w for w in pos_dict if w in text]
    neg_words = [w for w in neg_dict if w in text]
    pos_count = len(pos_words)
    neg_count = len(neg_words)
    return pos_count, neg_count

接下来，我们需要读入训练数据并进行预处理。假设训练数据为一个CSV格式的文件，其中包含两列，一列为文本内容，另一列为情感极性（0表示负面，1表示正面），代码如下：

# 读入训练数据
data = pd.read_csv('train_data.csv')

# 对文本进行分词
data['text'] = data['text'].apply(lambda x: ' '.join(jieba.cut(x)))

# 判断情感词并计数
data['pos_count'], data['neg_count'] = zip(*data['text'].apply(count_sentiment_words))

# 将情感极性转为0和1
data['polarity'] = data['polarity'].apply(lambda x: 1 if x == 'positive' else 0)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['polarity'], test_size=0.2, random_state=42)

接下来，我们需要对文本进行编码，将每个单词转化为一个数字。这里使用Keras中的Tokenizer类实现。我们需要指定词汇表的大小（即最多考虑多少个单词）、每个文本的最大长度等参数。代码如下：

# 对文本进行编码
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_train)

X_train_seq = tokenizer.texts_to_sequences(X_train)
X_test_seq = tokenizer.texts_to_sequences(X_test)

max_len = 200
X_train_pad = pad_sequences(X_train_seq, maxlen=max_len)
X_test_pad = pad_sequences(X_test_seq, maxlen=max_len)

接下来，我们可以构建CNN模型。模型包含一个嵌入层、一个卷积层、一个池化层、一个全连接层和一个输出层。代码如下：

# 构建模型
model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=128, input_length=max_len))
model.add(Conv1D(filters=64, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=4))
model.add(Dropout(0.2))
model.add(Dense(units=10, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

最后，我们可以使用训练集对模型进行训练，并在测试集上进行测试。代码如下：

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

# 在测试集上评估模型
score = model.evaluate(X_test_pad, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

完整代码如下：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, Conv1D, MaxPooling1D, Dropout
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import jieba

# 读入情感词典
sentiment_dict = pd.read_excel('情感词典.xlsx')

# 去除停用词
stopwords = pd.read_csv('stopwords.txt', header=None, sep='\t')
stopwords = list(stopwords[0])

sentiment_dict = sentiment_dict[~sentiment_dict['word'].isin(stopwords)]

# 分词
sentiment_dict['word'] = sentiment_dict['word'].apply(lambda x: ' '.join(jieba.cut(x)))

# 构建情感词典
pos_dict = sentiment_dict[sentiment_dict['positive'] == 1]['word'].tolist()
neg_dict = sentiment_dict[sentiment_dict['negative'] == 1]['word'].tolist()

# 定义函数，判断文本中的情感词并计数
def count_sentiment_words(text):
    pos_words = [w for w in pos_dict if w in text]
    neg_words = [w for w in neg_dict if w in text]
    pos_count = len(pos_words)
    neg_count = len(neg_words)
    return pos_count, neg_count

# 读入训练数据
data = pd.read_csv('train_data.csv')

# 对文本进行分词
data['text'] = data['text'].apply(lambda x: ' '.join(jieba.cut(x)))

# 判断情感词并计数
data['pos_count'], data['neg_count'] = zip(*data['text'].apply(count_sentiment_words))

# 将情感极性转为0和1
data['polarity'] = data['polarity'].apply(lambda x: 1 if x == 'positive' else 0)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['polarity'], test_size=0.2, random_state=42)

# 对文本进行编码
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_train)

X_train_seq = tokenizer.texts_to_sequences(X_train)
X_test_seq = tokenizer.texts_to_sequences(X_test)

max_len = 200
X_train_pad = pad_sequences(X_train_seq, maxlen=max_len)
X_test_pad = pad_sequences(X_test_seq, maxlen=max_len)

# 构建模型
model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=128, input_length=max_len))
model.add(Conv1D(filters=64, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=4))
model.add(Dropout(0.2))
model.add(Dense(units=10, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

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

# 在测试集上评估模型
score = model.evaluate(X_test_pad, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])