LAUSR

The goal of this paper is to conduct the study on the text classification for financial news based on partial information. By a fact that an indispensable step for the efficient use of topic information embedded in financial news is the text classification, a new neural network called “All Dataset based on CharCNN (Character Convolutional Neural Networks) and GRU (Gated Recurrent Unit)” (in short, AD-CharCGNN) which extracts a part of the financial article and incorporates both time domain and spatial domain to classify financial texts is proposed. In the study of this paper, we first build a character level vocabulary by reading all characters of the financial dataset, part of each financial text which will be classified is mapped to a high-dimensional spatial vector based on the vocabulary. Then, the vectors are convoluted in the spatial domain to get the text local features, and next, the features are processed by the gated recurrent units to get the features contained time information. Finally, the features which contain spatial and time information will be classified through softmax function to get the text classification results. Our results on the experiments confirm that the network proposed in this paper works effectively with the accuracy of 96.45%, and it seems that the text classification algorithm with the feature by taking only partial text part is more suitable for the application of the practice. Meanwhile, for the input with character level vector, the network is not only suitable for Chinese but also for other languages.