BTCAM: Attention-Based BiLSTM for Imbalanced Classification of Political Inquiry Messages

The classification of political inquiry messages is a crucial task in government affairs. However, with the increasing number of inquiry messages on platforms, it is difficult for government departments to accurately and efficiently categorize these messages solely through manual labor. Existing methods find it challenging to achieve excellent performance due to the sparsity of the features and the imbalanced data. In this paper, we propose a new framework (BTCAM) for automatically classifying political inquiry messages. Specifically, we first propose a topic-based data augmentation algorithm (TDA) to improve the diversity and quality of data. In addition, in order to enable the model to focus on the key information in the text, we propose an applicable streamlined convolutional block attention module (SCBAM), which can highlight the salient features on the channel and spatial axes. Extensive experiments show that the accuracy and recall of BTCAM reach 0.939 and 0.931, respectively, outperforming the state-of-the-art methods.