Year: 2025
Type: article
Abstract: Topic models are machine learning techniques that are able to discover themes in a set of documents.Turftopic is a topic modelling library including a number of recent developments in topic modelling that go beyond bag-of-words models and can understand text in context, utilizing representations from transformers.Turftopic focuses on ease of use, providing a unified interface for a number of different modern topic models, and boasting both model-specific and model-agnostic interpretation and visualization utilities.While the user is afforded great flexibility in model choice and customization, the library comes with reasonable defaults, so as not to needlessly overwhelm first-time users.In addition, Turftopic allows the user to: a) model topics as they change over time, b) learn topics on-line from a stream of texts, c) find hierarchical structure in topics, d) learning topics in multilingual texts and corpora.Users can utilize the power of large language models (LLMs) to give human-readable names to topics.Turftopic also comes with built-in utilities for generating topic descriptions based on key-phrases or lemmas rather than individual words.
DOI: 10.21105/joss.08183
Institution: Aarhus University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411979263