Have you ever spent hours fine-tuning parameters, only to get disappointing results in topic modeling? I’m guessing I’m not the only one who’s been there.
Recently, a fellow researcher shared their frustrating experience with BERTopic, a popular topic modeling technique. They were working with a dataset of 18,000 scientific abstracts on eye-tracking literature from Scopus. The goal was to identify underlying topics in the text. Sounds simple, right?
The Problem
The researcher was struggling with two main issues. Firstly, the topic assignments to documents didn’t fully capture the domain. And secondly, the average confidence score was a mere 0.37. That’s not exactly reassuring.
They tried tweaking parameters, but nothing seemed to work. It was as if they were chasing their tail, wasting precious time. The question was: is it worth the effort?
A Deeper Issue
The problem might not lie in the preprocessing or parameters. It’s possible that the dataset is too broad and unrelated. This got me thinking: how often do we blame the tools or our own skills when the issue lies in the fundamental nature of our data?
The Importance of Data Quality
Topic modeling is only as good as the data it’s fed. If your dataset is noisy, incomplete, or inconsistent, you can’t expect miracles from your algorithm. It’s essential to ensure that your data is high-quality, relevant, and well-structured.
Taking a Step Back
Before you throw in the towel, take a step back and assess your dataset. Ask yourself:
– Is my data clean and preprocessed correctly?
– Are there any inconsistencies or outliers that might be affecting my results?
– Is my dataset too broad or too narrow for the topic modeling technique I’m using?
By answering these questions, you might just find the root cause of your struggles. And who knows, you might even discover a new approach that yields better results.
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*Further reading: BERTopic Documentation*