# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 7 Conclusions  
By systematically evaluating the strengths and limitations of both transformer-based architectures, this study provides valuable insights into optimizing NLP models for classifying real-world news events in the context of conflict analysis. The study performed a comparative analysis of BERT and GPT for conflict-related text classification, evaluating their performance in both SL-MLG and MLC tasks. The experimental results indicate that GPT excelled in recall for MLC in the Sudan case. These findings highlight GPT's strength in flexible multi-label classification and BERT's reliability in well-defined, high-precision classification tasks. In both tasks, larger LLMs consistently outperformed their smaller counterparts. Llama3.1-70B under RAG achieved the second highest F1-score in MLC, while Llama3.3-70B and Llama3.1-70B under RAG ICL achieved the highest accuracy in SL-MLG. BERT, though outpaced in recall, remained highly precise and performed strongly, achieving the highest F1-score with the hyperparameter-tuned bert-large model achieving a score of 68.90% and achieving relatively strong accuracy scores in SL-MLG. RAG ICL achieved the most accurate results in SL-MLG, whereas standard RAG produced the second-best MLC performance in some configurations, showcasing the importance of prompting strategies. This suggests that definitions help constrain outputs in structured tasks but may limit flexibility in multi-label classification. Despite these plausible results, several challenges remain. First, handling ambiguous multi-label categories remains a challenge, especially when training data is limited or inconsistently labeled. In this study, overlapping event categories further complicated classification, making expert-defined definitions essential for guiding the model. Second, computational efficiency is a key concern, as BERT requires extensive training, and hyperparameter tuning is time intensive. GPT demands significant inference time for larger datasets, GPU acceleration is important for faster inferencing, and effective prompt engineering [14]. Third, dataset limitations, such as imbalanced category distributions and inconsistencies in human annotations, affect model performance and generalizability. Future research should explore advanced classification techniques such as hierarchical multi-label classification, contrastive learning, and semi-supervised learning to improve performance on limited datasets with complex category structures. Future work may also include a context-independent baseline such as FastText to better isolate the impact of contextual embeddings. While excluded in this study to maintain a focused comparison between transformer-based models, a future study on FastText could provide additional insight into the relative benefits of contextualization for conflict-related text classification. By enabling large-scale automated classification of conflict-related articles, this approach streamlines data collection pipelines for geospatial systems and enhances the utility of unstructured text in GIS-based analysis. Incident classification directly supports structured extraction of metadata for automatic mapping, trend analysis, and early warning systems. For planners and humanitarian organizations, the ability to consistently label incidents by type allows for improved situational awareness, resource allocation, and prediction of conflict dynamics. Integration of NLP classification into GIS frameworks not only improves responsiveness but also opens pathways for predictive modeling and automatic conflict mapping using spatiotemporal patterns in labeled conflict data. The constrained nature of the dataset, which has limited annotated examples and ambiguous multi-label categories, highlights the importance of selecting appropriate models and methods tailored to such scenarios.