  Topic modeling is pivotal in discerning hidden semantic structures within
texts, thereby generating meaningful descriptive keywords. While innovative
techniques like BERTopic and Top2Vec have recently emerged in the forefront,
they manifest certain limitations. Our analysis indicates that these methods
might not prioritize the refinement of their clustering mechanism, potentially
compromising the quality of derived topic clusters. To illustrate, Top2Vec
designates the centroids of clustering results to represent topics, whereas
BERTopic harnesses C-TF-IDF for its topic extraction.In response to these
challenges, we introduce "TF-RDF" (Term Frequency - Relative Document
Frequency), a distinctive approach to assess the relevance of terms within a
document. Building on the strengths of TF-RDF, we present MPTopic, a clustering
algorithm intrinsically driven by the insights of TF-RDF. Through comprehensive
evaluation, it is evident that the topic keywords identified with the synergy
of MPTopic and TF-RDF outperform those extracted by both BERTopic and Top2Vec.
