Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing
This work addresses the need for efficient news text classification in the news industry, but it is incremental as it combines existing deep learning techniques.
The paper tackled the problem of inefficient manual news text classification by proposing an automatic classification scheme using a Bi-LSTM and Attention Mechanism model, which significantly improved accuracy and timeliness while reducing manual intervention.
The development of Internet technology has led to a rapid increase in news information. Filtering out valuable content from complex information has become an urgentproblem that needs to be solved. In view of the shortcomings of traditional manual classification methods that are time-consuming and inefficient, this paper proposes an automaticclassification scheme for news texts based on deep learning. This solution achieves efficient classification and management of news texts by introducing advanced machine learning algorithms, especially an optimization model that combines Bi-directional Long Short-Term Memory Network (Bi-LSTM) and Attention Mechanism. Experimental results show that this solution can not only significantly improve the accuracy and timeliness of classification, but also significantly reduce the need for manual intervention. It has important practical significance for improving the information processing capabilities of the news industry and accelerating the speed of information flow. Through comparative analysis of multiple common models, the effectiveness and advancement of the proposed method are proved, laying a solid foundation for future news text classification research.