用于情感分类的双向深度LSTM

Deeply Hierarchical Bi-directional LSTM for Sentiment Classification

对商品、电影等的评论的体现人们对商品的喜好程度,从而为意向购买该商品的人提供参考,也有助于商家调整橱窗货品以取得最大利润。近年来,深度学习在文本上强大的表示和学习能力为理解文本语义、抓取文本所蕴含的情感倾向提供了极好的支持,特别是深度学习中的长短记忆模型(Long Short-Term Memory,LSTM)。评论是一种时序数据形式,通过单词前向排列来表达语义信息。而LSTM恰好是时序模型,可以前向读取评论,并把它编码到一个实数向量中,该向量隐含了评论的潜在语义,可以被计算机存储和处理。利用两个LSTM模型分别从前、后两个方向读取评论,从而获取评论的双向语义信息;再通过层叠多层双向LSTM来达到获取评论深层特征的目的;最后把这个模型放到一个情感分类模型中,以实现情感分类任务。实验证明,该模型相对基准LSTM取得了更好的实验效果,这表示双向深度LSTM能抓取更准确的文本信息。将双向深度LSTM模型和卷积神经网络(Convolutional Neural Network,CNN)进行实验对比,结果表明双向深度LSTM模型同样取得了更好的效果。

The comments on goods,films and others contribute to assess people＇s preference degree for goods,which provides reference for the people who intend to buy the goods,and can help businesses adjust shelves to maximize profits.In recent years,the powerful representation and learning ability in deep learning technologies provides a good support for understanding text semantics and grasping the emotional tendency of texts,especially the long short-term memory（LSTM）model in deep learning.The comment is a form of temporal data,which expresses semantic information through the forward arrangement of words.LSTM is a sequential model that reads the comment forward and encodes it into a real vector,and this vector implies the potential semantics of the comment and can be stored and processed by the computer.In this paper,two LSTM models are utilized to read comments from forward and backward directions respectively,and thus the two-way semantic information of the review can be obtained.Then the purpose of obtaining the deep features of comments is achieved by stacking the multilayer bidirectional LSTM.Finally,the model is put into a sentimental classification model to implement the sentiment classification.Experimental results show that the proposed method outperforms baseline LSTM,which means that deeply hierarchical bi-directional LSTM（DHBL）can capture more accurate text information.Compared with the convolutional neural network（CNN）model,the proposed model also achieves better effect.