Abstract
This study proposes a novel text classification model, MBConv-CapsNet, to address large-scale text data classification issues in the Internet era. Integrating the advantages of Mobile Inverted Bottleneck Convolutional Networks and Capsule Networks, this model comprehensively considers text sequence information, word embeddings, and contextual dependencies to capture both local and global information about the text effectively. It transforms from the original text matrix to a more compact and representative feature representation. A Capsule Network is designed to adaptively adjust the importance of different feature channels, including N-gram convolutional layers, selective kernel network layers, primary capsule layers, convolutional capsule layers, and fully connected capsule layers, aiming to enhance the model's ability to capture semantic information of text across different feature channels. The use of the sparsemax function instead of the softmax function for dynamic routing within the Capsule Network directs the network's focus more on capsules contributing significantly to the final output, reducing the impact of noise and redundant information, and further improving the classification performance. Experimental validation on multiple publicly available text classification datasets demonstrates significant performance improvements of the proposed method in binary classification, multi-classification, and multi-label text classification tasks, exhibiting better generalization capability and robustness.