请编写一段代码，用深度学习的方式能够实现用RNN进行中文短句的情感识别

好的，以下是一个简单的中文短句情感分类的RNN模型示例，使用了LSTM作为循环体。

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Embedding, LSTM, Dense

# 加载数据
with open('sentiment_dataset.txt', 'r', encoding='utf-8') as f:
    data = f.read()
sentences = data.split('\n')

# 构建词表
vocab = set()
for sentence in sentences:
    words = sentence.strip().split(' ')
    vocab.update(words)
vocab = sorted(vocab)
vocab_size = len(vocab)
word2idx = {w: i for i, w in enumerate(vocab)}
idx2word = {i: w for i, w in enumerate(vocab)}

# 构建输入输出数据
max_len = 20
X = np.zeros((len(sentences), max_len))
Y = np.zeros((len(sentences), 2))
for i, sentence in enumerate(sentences):
    words = sentence.strip().split(' ')
    for j, word in enumerate(words):
        if j >= max_len:
            break
        X[i, j] = word2idx[word]
    label = int(words[-1])
    Y[i, label] = 1

# 构建模型
embedding_size = 32
hidden_size = 64
model = tf.keras.Sequential([
    Embedding(vocab_size, embedding_size, input_length=max_len),
    LSTM(hidden_size),
    Dense(2, activation='softmax')
])
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

# 训练模型
batch_size = 128
epochs = 10
model.fit(X, Y, batch_size=batch_size, epochs=epochs, validation_split=0.1)

# 测试模型
test_sentence = '这个电影真的太好看了'
test_X = np.zeros((1, max_len))
words = test_sentence.strip().split(' ')
for j, word in enumerate(words):
    if j >= max_len:
        break
    test_X[0, j] = word2idx.get(word, 0)
pred = model.predict(test_X)[0]
print('Positive' if pred[1] > 0.5 else 'Negative')

以上代码仅供参考，具体的实现方式可以根据实际情况进行调整和优化。