Objectives:
The session aims to:

1. Compare Python-based topic modelling libraries, highlighting their relevance to real-world scenarios like policy analysis.
2. Explore practical use cases, including real-time document integration, tracking topic evolution, and finding similar documents.
3. Evaluate the tools based on performance, interpretability, scalability, and flexibility, with a focus on climate change policy data presented by [1] focusing on adaptation and mitigation.
4. Provide actionable guidance on selecting the right library for different project needs and datasets.

Outline:

1. Introduction to Topic Modeling: Overview of traditional and modern approaches, including their practical significance.

2. Algorithms & Libraries Overview: LDA (gensim) [2], CTM [3], Top2Vec [4], BERTopic [5]

3. Dataset and Use Cases:

   • Overview of the UK climate change policy dataset.
   • Use cases inspired by DEFRA and similar organisations, such as:
     - Real-time integration for continuously adding new documents.
     - Tracking topic development over time (dynamic topic modeling).
     - Retrieving similar documents for faster insights.
     (- Classification)

4. Evaluation Criteria: Analysis of libraries based on:
   - Ease of Use: How easy it is for no coding experts
   - Quality: Coherence and diversity of extracted topics.
   - Efficiency: Runtime performance and scalability.
   - Flexibility: Features like contextual embeddings and integration capabilities.
   - Interpretability: Ease of understanding topics and output.

5. Results: Detailed findings, including specific advantages and limitations of each library in supporting the outlined use cases.

6. Practical Recommendations: Guidance on choosing a library based on project goals, dataset characteristics, and organisational needs.

7. Conclusion and Future Directions: Summary of key insights and the evolving role of embedding-based methods in topic modelling.

Outcomes:
By attending this session, participants will:

• Gain an in-depth understanding of Python’s top topic modeling libraries.
• Learn how to apply these tools to real-world challenges in policy analysis and other fields.
• Understand how to handle use cases like real-time document integration and topic evolution over time.
• Develop the skills to evaluate and choose the best tool for specific datasets and objectives.

Target Audience

This talk is for:
- Data scientists and NLP practitioners seeking to apply topic modelling to unstructured text data.
- Policy analysts and researchers working with large textual datasets, such as government or environmental policies.
- Professionals in organisations like DEFRA, where tracking changes, adding new documents, or finding similar records are critical tasks.
- Python enthusiasts interested in cutting-edge NLP techniques for extracting meaningful insights.

[1] R. Biesbroek, S. Badloe, and I. Athanasiadis. Machine learning for research on cli-
mate change adaptation policy integration: an exploratory uk case study. Regional
Environmental Change, 20, 07 2020.

[2] https://pypi.org/project/gensim/
[3] https://github.com/MilaNLProc/contextualized-topic-models
[4] https://github.com/ddangelov/Top2Vec
[5] https://maartengr.github.io/BERTopic/index.html
5 https://github.com/MilaNLProc/contextualized-topic-models