Enhanced analysis of large-scale news text data using the bidirectional-Kmeans-LSTM-CNN model.

ABSTRACT: Traditional methods may be inefficient when processing large-scale data in the field of text mining, often struggling to identify and cluster relevant information accurately and efficiently. Additionally, capturing nuanced sentiment and emotional context within news text is challenging with conventional techniques. To address these issues, this article introduces an improved bidirectional-Kmeans-long short-term memory network-convolutional neural network (BiK-LSTM-CNN) model that incorporates emotional semantic analysis for high-dimensional news text visual extraction and media hotspot mining. The BiK-LSTM-CNN model comprises four modules: news text preprocessing, news text clustering, sentiment semantic analysis, and the BiK-LSTM-CNN model itself. By combining these components, the model

SUBMITTER: Zeng Q 

PROVIDER: S-EPMC11323039 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature