MaartenGr / MaartenGr/BERTopic
Normalizing Topic Frequency Based on the Number of Documents
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- Dominant language
- Python
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Description
Hi,
We have a similar number of daily documents in our application. We use BERTopic to create a 'topics over time' plot. When we create the model and plot in the first half of the month, topic frequency in the last month drops significantly compared to previous months. Waiting until month-end to create the model yields a more reasonable plot.
Do you recommend normalizing topic frequency by dividing it by the number of documents in the corresponding month? Your insights would be valuable.
Contributor guide
First steps
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Research direction
No files, tests, or entry points are named. First inspect the topics-over-time plotting path and how topic frequency is calculated for each period. Done would require a decided normalization approach, documented expected behavior, and validation that plots remain comparable when the model is created before month-end.
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Assessment
- Tech stack
- python
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100