In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models.

近年来，基于神经网络的模型（例如机器学习和深度学习）在文本分类中取得了出色的成绩。在营销意图检测的研究中，采用分类手段对具有营销意图的新闻进行识别。但是，当前的大多数新闻都以对话框的形式出现。寻找新闻句子之间的潜在相关性以确定潜在语义存在一些挑战。为了解决这个问题，本文提出了一种基于CLSTM的主题记忆网络（简称为CLSTM-TMN）用于营销意图检测。提出了ReLU-神经主题模型（RNTM）。构造了一个隐藏层以有效捕获主题文档表示形式，并应用潜在变量来增强主题模型学习的粒度。我们更改了当前神经主题模型（NTM）的结构，以添加CLSTM分类器。这种方法是长期记忆（LSTM）和卷积神经网络（CNN）的新组合集合。CLSTM结构具有从一系列文本输入中查找关系的能力，并具有通过卷积运算提取局部特征和密集特征的能力。实验证明了该方法对营销意向检测的有效性。与其他比较模型相比，我们的检测模型在F1方面的改进更为显着（7％）。通过卷积运算提取局部和密集特征的能力。实验证明了该方法对营销意向检测的有效性。与其他比较模型相比，我们的检测模型在F1方面的改进更为显着（7％）。通过卷积运算提取局部和密集特征的能力。实验证明了该方法对营销意向检测的有效性。与其他比较模型相比，我们的检测模型在F1方面的改进更为显着（7％）。