Advanced topic modeling using Listly

Finding patterns in chaotic data, people crumble and usually do not perform well. Luckily we have machines to learn and guide us through this chaos.
The same happens with topic modeling in data science, where we try to find patterns and topics in different fields to get insights and better understand the data.

Topic modeling is like a treasure hunt for hidden gems in a pile of words! It’s an amazing technique that uncovers hidden themes in texts, revealing exciting insights that are not visible at first. Imagine unraveling patterns and grouping related documents together effortlessly.
Let’s dive into the world of topic modeling and unleash the power of words!

Photo by Amador Loureiro on Unsplash (edited by author)

What is topic modeling?

We use topic modeling to find the main points of conversations in different data. The goal is to establish critical points that define different categories and, as such, analyze them further.
For example, in a large news set, we want to find out the main categories and the topics most relevant for a particular day.
Large amounts of text are analyzed, and the main happenings are found. That defines what kind of day it is from that news outlet.
Let’s see how we can do this in a practical example with company reviews.