## 3 Methodology
### ChatGPT-Based Keyphrase Extraction and Classification  
KPE is an automated process of identifying the most relevant and representative phrases from text input. Although research on the development of a KPE model by itself is an interesting research object, this study utilized an existing text model for KPE. Recent large language models have shown superior performance on NLP tasks, including the KPE task [25]. There are two representative text models in the KPE tasks, namely, ChatGPT [26] and KeyBART [27]. This study employed the ChatGPT model as a keyphrase generator to identify points of conflict in relation to infrastructure from a collection of news articles for the following reasons. First, the training datasets of ChatGPT are multi-domain documents while that of KeyBART are scientific documents. Thus, ChatGPT covers a wider range of topics compared to KeyBART in terms of natural language understanding. Second, the maximum length of input tokens of ChatGPT is 4096 for the GPT-3.5 version, while that of KeyBART is 1024. Therefore, ChatGPT can be utilized with longer texts such as scientific documents or news articles. SemEval2010 is a widely used long scientific document dataset for KPE tasks, and DUC2001 is a dataset for KPE consisting of long news articles. ChatGPT had markedly better performance for both representative datasets due to its higher input token limit [25]. Third, ChatGPT is adjustable as the keyphrases are generated by user prompts, while KeyBART focuses on extracting keyphrases related to the main theme of input text. Appropriate keyphrases within a text may vary depending on the perspective. Owing to the intrinsic properties of its conversation-based generative AI, a user can guide ChatGPT in extracting keyphrases from a predefined perspective. In this way, ChatGPT performs KPE specific to any purpose without additional fine-tuning. Since this study aimed to identify domain-specific keyphrases focusing on conflict from long news articles, the authors determined that ChatGPT is more appropriate than KeyBART.  
KPC is a process to classify the extracted keyphrases into predefined categories, which is a kind of text classification task in NLP. Most existing studies developed text models for text classification based on data in accordance with the purpose of each study [28]. However, the previous approach suffered from the lack of data and labor-intensive manual annotation to train a model [29]. Meanwhile, recent attempts aiming to evaluate the performance of ChatGPT reported that ChatGPT outperforms manual annotation in text classification [30; 31]. Thus, this study utilized ChatGPT for not only KPE but also KPC.  
ChatGPT is a state-of-the-art deep learning model for natural language understanding and generation introduced by OpenAI [26]. As its name states, the generative pre-trained  
Figure 1: Proposed keyphrase extraction and classification process.  
transformer (GPT) is a pretrained language model designed to predict which word will most appropriately follow a given text. ChatGPT is a fine-tuned model of the GPT series that generates coherent and contextually relevant responses to a given text called a prompt. ChatGPT is able to process various NLP tasks in accordance with a user request through prompt engineering. There are two kinds of prompts: system prompts and user prompts. The system prompt is a pre-configured text that is sent to the model when a new interaction starts. A user can instruct the model on the task it should perform through a system prompt. The user prompt is text that a user inputs to elicit a response from the model. The model takes this user prompt and generates a response based on pretrained data [32]. This study first asked ChatGPT to extract conflict-related keyphrases from a given article. Then, the authors required ChatGPT to classify the key phrases, which is the response of the previous step, into one of the predefined categories.