Developing a conceptual framework for short text categorization using hybrid CNN- LSTM based Caledonian crow optimization
作者：
Highlights：
• To develop conceptual framework using four phases for short text categorization.
• To propose hybrid convolutional CNN-LSTM for classifying the short text.
• To utilize Caledonian crow optimization algorithm for optimizing the weight of CNN.
• Getting attracted to the learning strategy of crows the NC2LO algorithm is employed.
• Dataset based on online reviews is used to identify positive and negative reviews.
摘要
•To develop conceptual framework using four phases for short text categorization.•To propose hybrid convolutional CNN-LSTM for classifying the short text.•To utilize Caledonian crow optimization algorithm for optimizing the weight of CNN.•Getting attracted to the learning strategy of crows the NC2LO algorithm is employed.•Dataset based on online reviews is used to identify positive and negative reviews.
论文关键词：Short text,Convolutional neural network,New Caledonian crow optimization,Long short term memory
论文评审过程：Received 27 September 2021, Revised 21 July 2022, Accepted 9 August 2022, Available online 13 August 2022, Version of Record 16 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118517
