How Apple Podcasts Tags your Episodes with Wikipedia Topics

Apple Podcasts started to automatically transcribe podcast episodes. With the help of various NLP techniques and transcription data, the episodes are then associated to a variety of Wikipedia topics.

How does Apple tag episodes?

Probably with Topic Modeling. Topic modeling is a technique used in natural language processing (NLP) to automatically identify and organize topics within a collection of text. This is done by analyzing the words and phrases used in the text and grouping them into topics based on their commonality.

Topic modeling can be a powerful tool for a variety of applications, such as analyzing customer feedback to identify common themes and concerns, or organizing a large collection of documents for easier search and retrieval.

One popular algorithm for topic modeling is Latent Dirichlet Allocation (LDA), which is a generative statistical model that assumes each document is a mixture of a small number of topics, and each word in the document is generated from one of those topics.

To use LDA for topic modeling, the text must first be preprocessed to remove stop words and perform stemming (reducing words to their base form) in order to reduce the size of the resulting topic model. Then, the LDA algorithm can be applied to the preprocessed text to identify the underlying topics.

Once the topics have been identified, they can be used to organize the text into groups based on the commonality of their words and phrases. This can make it easier to search and analyze the text, and can also provide valuable insights into the content of the text.

Overall, topic modeling is a valuable tool for natural language processing and can be applied to a wide range of applications. By automatically identifying and organizing topics within a collection of text, it can help make large amounts of text more manageable and easier to analyze.

The cool thing about a structural topic model is that you can add metadata to the probability calculation which can lead to a significant accuracy improvement. Take the term “digital currency” and think about its meaning pre and post-bitcoin launch. Apple can take the metadata “episode publishing date” and assign the topics “digital payment system” and “cryptocurrency” accordingly. Same with metadata that describes the podcast channel, the podcast host, podcast guests, etc.

Why does Apple tag episodes with topics?

Some possible reasons:
– To improve their podcast recommendation engine
– To improve their podcast search engine
– To create listening profiles (target users based on their listening activity on each topic), demographical statistics, etc.

🎉  Podkite released an associated topics chart with aggregated podcast channel-level data. See if your episodes are tagged with topics. It’s available on all premium plans starting at two Matcha Lattes per month.