# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 4 Conclusions  
This study presents TR-GPT-CF, a novel approach for post-extracted topic refinement. It employs Z-score centroid-based misaligned word detection and a hybrid semantic-contextual approach for word replacement, which utilizes WordNet and GPT. To evaluate the effectiveness of our refinement method, we applied four topic modeling techniques--LDA, NMF, BERTopic, and Gaussian-BAT--across six datasets: AGNews, TagMyNews, Yahoo Answers, Newsgroup, SMS Spam, and Kaggle's Science Articles. Using these four topic modeling techniques and six datasets, we evaluated the extracted topics by calculating the percentage improvement in coherence before and after applying the refinement method. In addition, we have investigated the enhancement of topic coherence across six datasets through the implementation of various candidate word generation techniques, including WordNet, GPT, and a combination of both. Each strategy has its own benefits in aligning topic words more accurately, making the topics easier to understand. Through the comparison of different approaches, we hope to show how well each one assists in rendering topics more logical and significant.  
TR-GPT-CF demonstrates enhancements across all datasets. It is highly effective at refining coherence in simpler datasets with less linguistic complexity. Furthermore, it effectively improves coherence for moderately structured datasets, rendering it appropriate for semi-structured data. Building on this foundation, the combination approach of WordNet and GPT consistently provides the most significant improvements in topic coherence across diverse datasets. This is attributed to WordNet's semantic grounding and GPT's contextual adaptability. The synergy between the two addresses both semantic precision and contextual fluency, making it robust for both structured and informal datasets. The combined approach is highly recommended for challenging datasets that need both domain knowledge and contextual fluency. Individual approaches such as WordNet or GPT alone may suffice in straightforward datasets where only one aspect--either semantics or context--is critical. This highlights the importance of our work, as the human evaluation of models is both costly and time-intensive, underscoring the value of our efficient, automated solutions.  
Our future work will focus on further automating the review process and broadening our methodologies to encompass a wider range of datasets. We will also investigate the use of in-context learning paradigms to generate alternative words as a means to enhance the quality of GPT's responses.  
**Author Contributions:** Conceptualization, H.-S.Y.; methodology, I.W.; software, I.W.; validation, H.-S.Y. and I.W.; formal analysis, I.W.; investigation, I.W.; data curation, I.W.; writing--original draft preparation, I.W.; writing--review and editing, I.W. and H.-S.Y.; visualization, I.W.; supervision, H.-S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work is partially supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143782).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.  
**Acknowledgments:** Some portions of code assistance and alternative word generation were facilitated using OpenAI's ChatGPT 4.0 and model GPT-3.5-_turbo-1106_. However, all final interpretations, decisions, and evaluations were conducted by the authors, with the content and results being fully reviewed and verified by them.  
**Conflicts of Interest:** The authors declare no conflicts of interest.