LLM-Assisted Topic Reduction for BERTopic on Social Media Data
This work addresses topic modeling challenges for social media analysts, but it is incremental as it builds on existing BERTopic and LLM methods.
The paper tackled the problem of excessive overlapping topics in BERTopic when applied to noisy social media data by proposing a framework that combines BERTopic for topic generation with large language models for topic reduction, resulting in improved topic diversity and often coherence across three Twitter/X datasets.
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.