基于CNN和LSTM的异构数据舆情分类方法

Public Opinion Classification of Heterogeneous Data Based on CNN and LSTM

随着网络的发展,网络舆情数据呈现出爆炸式增长的趋势.使得数据类型越来越复杂,这些网络数据相互结合,构成了一个复杂的数据结构来表达数据的信息.在舆情数据中,通过单一类型的数据(图片、文本、语音等)越来越难以完整的表达数据信息.对于一个包含多种类型数据的网络信息,本文提出一种新的舆情分类模型,通过神经网络模型分别去学习不同类型信息的数据特征,对它们的特征融合后进行分类,通过这种方法实现数据信息更好地分类.在实验中,本文分别使用LSTM和CNN神经网络提取文本和图像数据特征,对二者特征融合后进行分类.结果证明,多种类型的数据特征进行融合后再分类,可以更好地实现对网络舆情数据信息的分类,提高了舆情信息分类的准确性.

With the development of the network, the public data which shows the trend of explosive growth, making the data type more and more complex. These network data combine with each other to form a complex network data structure to express the information of data. In this scenario, it is increasingly difficult to fully express data information through a single type of data (picture, text, voice, etc.). For the purpose of a network information that contains multiple types of data can be classified better, this study proposes a new public opinion classification model via neural network which is used to learn the data features respectively, and to classify their features after fusion. In the experiment, LSTM and CNN neural networks are used to extract text and image’s features, fusing the two features to classified. The experimental results show that the reclassification after the fusion of various data features can better realize the classification and improve the accuracy of data information classification.