Data Science

Every morning, my RSS reader greets me with hundreds of new posts. Tech blogs, indie developers’ journals, photography content - they all compete for attention. While I’ve gotten good at quickly scanning through these feeds , I keep wondering about all the great content I might be missing from sources I’ve had to ignore simply because their signal-to-noise ratio doesn’t justify daily checking. On the other hand, the posts that I shortlist from my RSS feeds and read or listen to, end up on a curated repository of articles that have passed my personal quality threshold, so I have access to a valuable collection of content (on Pocket ) that is relevant to my interests. This made me wonder, can I utilize this data, and create a content recommendation system tailored to my preferences? Can I build a system that would review new posts from feeds where only 1 in 20 posts might match my content priorities, and filter those for me? ...

I’ve been recently working on survey response data that in addition to aggregatable question types like Likert-scale and multiple-choice questions, includes optional free-text questions. Although we are lucky that thousands of the respondents spend time elaborating on questions and leaving comprehensive free-text responses, getting insights from these text responses is challenging. While investigating how to enrich this text data with proper metadata related to their topics, I came across BERTopic which introduces itself as a topic modeling technique to create clusters allowing for easily interpretable topics. In this post, I’ll explore BERTopic and will go through an example to explain what adjustments worked for me. ...