A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting

Cui, Ligang; Chen, Yingcong; Deng, Jie; Han, Zhiyuan

doi:10.1016/j.eswa.2024.124409

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.124409

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A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting

Ligang Cui,

Yingcong Chen,

Jie Deng

et al.

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

Zeng

2024

PeerJ Computer Science

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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 effectively identifies common features within the input data, clusters similar news articles, and accurately analyzes the emotional semantics of the text. This comprehensive approach enhances both the accuracy and efficiency of visual extraction and hotspot mining. Experimental results demonstrate that compared to models such as Transformer, AdvLSTM, and NewRNN, BiK-LSTM-CNN achieves improvements in macro accuracy by 0.50%, 0.91%, and 1.34%, respectively. Similarly, macro recall rates increase by 0.51%, 1.24%, and 1.26%, while macro F1 scores improve by 0.52%, 1.23%, and 1.92%. Additionally, the BiK-LSTM-CNN model shows significant improvements in time efficiency, further establishing its potential as a more effective approach for processing and analyzing large-scale text data

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

Zeng

2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

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A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting

Cited by 1 publication

References 76 publications

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

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

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