#topic modeling

Explore Tomotopy: a Python extension for the Gibbs-sampling based library Tomoto, optimized for fast and efficient topic modeling. With SIMD instruction support and compatibility across Linux, macOS, and Windows, Tomotopy offers LDA models like Labeled LDA and Supervised LDA. It facilitates faster iterations using multicore CPUs. Installation is straightforward, with options to save and load models easily. The interactive model viewer provides visual analysis, complemented by comprehensive documentation and robust features suitable for NLP research and applications.

Explore BERTopic for topic modeling using transformers and c-TF-IDF. It supports supervised and semi-supervised models, offering hierarchical and zero-shot topic solutions. With modular workflows and multi-language support, BERTopic enhances text analysis across diverse datasets, providing rich visualizations and seamless integration.

Enhancing multilingual topic coherence with Contextualized Topic Models using BERT. These models integrate contextual and traditional bag-of-words approaches, using CombinedTM for coherence and ZeroShotTM for language diversity. Adaptable to any pre-trained embedding, this framework supports cutting-edge topic modeling. Emphasizing multilingual applications, it predicts topics in unseen data efficiently. Detailed tutorials and documentation support both language-specific and cross-lingual tasks. Discover intuitive human-in-the-loop classification with Kitty, swiftly identifying document clusters. This open-source project benefits from community support and is available under the MIT license.