Most listed US companies host earnings calls every quarter. These are conference calls where management discusses financial performance and company updates with analysts, investors and the media. Earnings calls are important — they highlight valuable information for investors and provide an opportunity for interaction through Q&A sessions.

There are hundreds of earnings calls held each quarter, often with the release of detailed transcripts. But the sheer volume of those transcripts makes analyzing them a daunting task.

Topic modeling is a way to streamline this analysis. It’s an area of natural language processing that helps to make sense of large volumes of text data by identifying the key topics or themes within the data.

In this article, I show how to apply topic modeling to a set of earnings call transcripts. I use a popular topic modeling approach called Latent Dirichlet Allocation and implement the model using Python.

I also show how topic modeling can require some judgement, and how you can achieve better results by adjusting key parameters.

#text-analytics #naturallanguageprocessing #earnings-call #machine-learning #topic-modeling