Bertopic coherence, - "Coherence based Document Clustering" First, BERTopic is applied to air pollution environmental regulation policies using the qwen3-embedding model to represent long documents. 24% improvement in topic coherence compared to the default BERTopic framework, showcasing the potential value of the optimized BERTopic framework for enhancing the effectiveness of unsupervised topic modeling. Apr 8, 2021 · The following steps should be the correct ones in calculating the coherence scores. However, it might be the case that another classification metric would be more suitable. May 12, 2023 · I am currently looking at how to derive Topic coherence scores from my model. 8501 across 197 evaluated topics. Although NQTM, BERTopic, and ECRTM achieve higher topic diversity scores t an FASTM, they underperform in coherence metrics, sacri cing topic interpretability. Dec 20, 2022 · In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even In BERTopic, there are a number of different topic representations that we can choose from. Mar 15, 2024 · BERTopic exhibited best coherence when 80% of dataset was retained (20% of data, marked as outlier, was removed). pi 130 mance, with an average c v coherence score of 0. Jul 1, 2025 · Subsequently, BERTopic applies HDBSCAN, a density-based clustering algorithm, to cluster documents and identify the key topics for each cluster. THE PRESENTED METHOD IS ABBREVIATED TO CBC. Also, make sure to build the tokens with the exact same tokenizer as used in BERTopic. May 23, 2024 · Experimental results demonstrate a 12. e THUCNews dataset, FASTM out-performs other models on most topic coherence metrics. Some additional preprocessing is necessary since there is a very small part of that in BERTopic. Jul 1, 2025 · BERTopic harnesses pre-trained language models, such as BERT, to embed textual data prior to the application of clustering techniques for topic extraction. TOP2VEC AND BERTOPIC BOTH UTILIZE HDBSCAN FOR FINDING THE NUMBER OF TOPICS. . As a consequence, BERTopic was run, limited to producing a maximum of 200 clinical 127 (HDBScan and BERTopic parameters are shown in supplementary material tables A1, 128 A2). They are all quite different from one another and give interesting perspectives and variations of topic representations. By generating embeddings that encapsulate contextual information, BERTopic produces more nuanced and meaningful topics than LDA. Unlike LDA, which focuses on individual lexical items, BERTopic considers the contextual and semantic relationships between words, offering a more sophisticated topic structure. However, TD showed best performance for almost 100% of data. Of TABLE II DATA SET COMPARISON FOR ALL USED METHODS. This study carries out a comparative assessment of two cutting-edge unsupervised topic modelling algorithms: BERTopic based on bidirectional encoder representations from transformers (BERT), and latent Dirichlet allocation (LDA). This improves semantic representation beyond bag-of-words features and alleviates the short text limitation common in prior BERTopic applications, enhancing topic coherence and interpretability. FOR LDA AND NMF COHERENCE SCORES ARE USED FOR FINDING THE OPTIMAL NUMBER OF TOPICS. This re ects FASTM's moder-ate control over diversity while maintaining high-quality topics, avoiding the 125 event).


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