# BTCAM: Attention-Based BiLSTM for Imbalanced Classification of Political Inquiry Messages
## 5 Conclusions and Future Work  
In this study, we develop a deep learning framework (BTCAM) based on an improved bidirectional LSTM that can automatically classify unbalanced government query information. In the BTCAM framework, we first propose our topic-based data augmentation algorithm. Different from existing algorithms, this algorithm pays more attention to topic information when generating augmented data, which not only improves the quality of augmented data but also greatly improves efficiency and solves the imbalance problem. Secondly, in the dual-gated convolutional mechanism module, we develop a streamlined convolutional block attention mechanism, which can effectively improve the generalization ability of the model. Extensive experiments have shown that the proposed BTCAM achieves superior accuracy and recall in the automatic classification of political inquiry messages.  
Although the proposed model exhibits excellent prediction capabilities, it currently relies solely on traditional vectorization methods. In future work, we will explore the incorporation of pre-trained language models to extract word vectors imbued with rich contextual semantics. These context-aware embeddings will be utilized as supplementary feature inputs, fused with conventional vector representations before being fed into the model. This integrated approach aims to leverage the nuanced linguistic understanding of pre-trained architectures while retaining the established strengths of traditional vectoriza  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Execute Time** & **FastText** & **TextCNN** & **BiLSTM\_ATT** & **TextRNN** & **BTCAM** \\ \hline EDA & 10488 s & 0.592 & 0.721 & 0.720 & 0.761 & 0.791 \\ AEDA & 8.36 s & 0.613 & 0.742 & 0.884 & 0.881 & 0.897 \\ TDA & 129 s & 0.910 & 0.957 & 0.961 & 0.968 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Execution time and performance on POData. Execution time comparison for adding 15 augmented sentences per message using EDA, AEDA, and TDA. And the average accuracy of the augmented data in the classification model.  
Figure 9: Visual analysis of embeddings on POData augmented by (**a**) EDA, (**b**) AEDA, and (**c**) TDA.  
tion methods, thereby further enhancing the model's performance in nuanced government consultation information classification tasks.  
Conceptualization, H.H. and D.Z.; methodology, H.H., C.W. and D.Z.; validation, H.H. and C.W.; formal analysis, H.H. and C.W.; investigation, H.H.; resources, D.Z.; data curation, H.H.; writing--original draft preparation, H.H.; writing--review and editing, H.H.; visualization, H.H.; supervision, D.Z.; project administration, D.Z.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by Artificial Intelligence Robot Industry College of South China Normal University (Guangdong Provincial Model Industry College), and Scientific Research Innovation Project of Graduate School of South China Normal University (2025 KYLX068).  
Not applicable.  
The POData supporting the conclusions of this article will be made available at GitHub.  
The authors declare no conflicts of interest.