## 5 Results  
This study evaluated the KPC performance by calculating the precision, recall, and F1-score. The F1-score is a metric that combines both precision and recall, providing a single measure of a model's performance. The F1-score ranges from 0% to 100%, where a score of 100% indicates perfect precision and recall, and a score of 0% indicates that neither precision nor recall has been achieved. Precision, recall, and F1-score are based on a confusion matrix which consists of true positive (TP), false positive (FP), true negative (TN), and false negative (FN), as described in Table 3.  
_Precision_ is the ratio of _TP_ predictions to all the samples a model predicted as positive (Equation (1)), while _recall_ is the ratio of _TP_ predictions to all the actual positive samples (Equation (2)). The _F1-score_ is a harmonic mean of _precision_ and _recall_ (Equation (3)). In addition, there are three ways to calculate the average, namely, micro average, macro average, and weighted average. The micro average considers all categories collectively; the macro average calculates the metric independently for each category and then takes the \begin{table}
\begin{tabular}{c c c} \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline Actual positive & True positive (TP) & False negative (FN) \\ Actual negative & False positive (FP) & True negative (TN) \\ \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix. average; and the weighted average takes the average using the number of samples in each category as weights. \[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]  
Table 4 shows the performances of KPC using ChatGPT. The micro, macro, and weighted average F1-scores were 85.7%, 83.6%, and 84.7%, respectively. As a result, the ChatGPT-based KPC showed a notable performance in identifying conflict drivers from news articles. In particular, ecological issue (D-03), opposite movement and response (D-07), perception and emotional issue (D-08), professional investigation issue (D-09), project organization issue (D-11), and public-private partnership issue (D-12) showed a good performance with F1-scores over 90%. Meanwhile, compensation issue (D-02), project objective issue (D-10), and technical issue (D-17) showed a somewhat insufficient performance with F1-scores below 80%. In addition, the recall of 15 conflict drivers was over 90% and that of all conflict drivers except four factors (D-07, D-08, D-09, and D-14) was computed to be greater than or equal to the precision value; that is, the ChatGPT-based KPC is practical for detecting actual conflict drivers with high performance. The authors compared the KPC performance with other models that aimed to recognize words or phrases and classify them into predefined categories in the construction domain (Table 5). The performance of word/phrase classification varies depending on multiple factors such as research objective, document type, the number of classes, language, and sample distribution [39]. KPC in this study resulted in suboptimal performance compared to other research records. This could be attributed to the variability of the classification object. Keyphrases used in this study consisted of various lengths from two words to a full sentence. Also, the keyphrase distribution was imbalanced, which might disrupt the text classification performance. Nevertheless, the performance of KPC in this study is notable in that it utilized existing LLM without further training. Developing a domain-specific