LLM-Assisted Topic Reduction for BERTopic on Social Media Data

• URL: http://arxiv.org/abs/2509.19365v1
• Date: Thu, 18 Sep 2025 20:59:11 GMT
• Title: LLM-Assisted Topic Reduction for BERTopic on Social Media Data
• Authors: Wannes Janssens, Matthias Bogaert, Dirk Van den Poel,
• Abstract summary: We propose a framework that combines BERTopic for topic generation with large language models for topic reduction.<n>We evaluate the approach across three Twitter/X datasets and four different language models.
• Score: 0.22940141855172028
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The BERTopic framework leverages transformer embeddings and hierarchical clustering to extract latent topics from unstructured text corpora. While effective, it often struggles with social media data, which tends to be noisy and sparse, resulting in an excessive number of overlapping topics. Recent work explored the use of large language models for end-to-end topic modelling. However, these approaches typically require significant computational overhead, limiting their scalability in big data contexts. In this work, we propose a framework that combines BERTopic for topic generation with large language models for topic reduction. The method first generates an initial set of topics and constructs a representation for each. These representations are then provided as input to the language model, which iteratively identifies and merges semantically similar topics. We evaluate the approach across three Twitter/X datasets and four different language models. Our method outperforms the baseline approach in enhancing topic diversity and, in many cases, coherence, with some sensitivity to dataset characteristics and initial parameter selection.

Related papers

• TopiCLEAR: Topic extraction by CLustering Embeddings with Adaptive dimensional Reduction [0.0]
  We present TopiCLEAR: Topic extraction by CLustering Embeddings with Adaptive dimensional Reduction.<n>We evaluate our approach on four diverse datasets, 20News, AgNewsTitle, Reddit, and TweetTopic.<n>Our method produces more interpretable topics, highlighting its potential for applications in social media data and web content analytics.
  arXiv  Detail & Related papers  (2025-12-07T07:01:28Z)
• CAST: Corpus-Aware Self-similarity Enhanced Topic modelling [16.562349140796115]
  We introduce CAST: Corpus-Aware Self-similarity Enhanced Topic modelling, a novel topic modelling method.<n>We find self-similarity to be an effective metric to prevent functional words from acting as candidate topic words.<n>Our approach significantly enhances the coherence and diversity of generated topics, as well as the topic model's ability to handle noisy data.
  arXiv  Detail & Related papers  (2024-10-19T15:27:11Z)
• Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms [6.349503549199403]
  This study introduces an innovative end-to-end semantic-driven topic modeling technique for the topic extraction process. Our model generates document embeddings using pre-trained transformer-based language models. Compared to ChatGPT and traditional topic modeling algorithms, our model provides more coherent and meaningful topics.
  arXiv  Detail & Related papers  (2024-09-30T18:15:31Z)
• Explaining Datasets in Words: Statistical Models with Natural Language Parameters [66.69456696878842]
  We introduce a family of statistical models -- including clustering, time series, and classification models -- parameterized by natural language predicates.<n>We apply our framework to a wide range of problems: taxonomizing user chat dialogues, characterizing how they evolve across time, finding categories where one language model is better than the other.
  arXiv  Detail & Related papers  (2024-09-13T01:40:20Z)
• Interactive Topic Models with Optimal Transport [75.26555710661908]
  We present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
  arXiv  Detail & Related papers  (2024-06-28T13:57:27Z)
• Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations [35.74225306947918]
  We propose a joint latent space learning and clustering framework built upon PLM embeddings. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery.
  arXiv  Detail & Related papers  (2022-02-09T17:26:08Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Modeling Topical Relevance for Multi-Turn Dialogue Generation [61.87165077442267]
  We propose a new model, named STAR-BTM, to tackle the problem of topic drift in multi-turn dialogue. The Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-27T03:33:22Z)
• Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling [81.33107307509718]
  We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
  arXiv  Detail & Related papers  (2020-08-11T03:55:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.