W-RNN: News text classification based on a Weighted RNN

Most of the information is stored as text, so text mining is regarded as having high commercial potential. Aiming at the semant ic constraint problem of classification methods based on sparse representation, we propose a weighted recurrent neural network (W - RNN), which can fully extract text serialization semantic information. For the problem that the feature high dimensionality an d unclear semantic relationship in text data representation, we first utilize the word vector to represent the vocabulary in the text and use Recurrent Neural Network (RNN) to extract features of the serialized text data. The word vector is then automatica lly weighted and summed using the intermediate output of the word vector to form the text representation vector. Finally, the neural network is used for classification. W - RNN is verified on the news dataset and proves that W - RNN is superior to other four b aseline methods in Precision, Recall, F1 and loss values, which is suitable for text classification. On account of the certainty and comprehensibility of its expression, text has bec ome the popular way of information expression and transmission. Text classification is an extremely important research direction [1].