Ramgopal Prajapat:

Learnings and Views

AI and ML Applications in Media Industry

By: Ram on Jan 28, 2023


Some of the top AI/ML Use-cases specific to Media & Entertainment industry are:

  1. Meta Tagging using AI/ML for Search and Content Intelligence
  2. Summary Highlights using Deep Learning
  3. Automated Article based on Video Contents
  4. Compliance Improvement using AI/ML
  5. Ads Personalization
  6. Content Personalization and Recommendations
  7. Subtitling and Translations
  8. Search and SEO



There are multiple dimensions to Media and Entertainment industry. Some of the organizations within Media & Entertainment are acting as distribution channels of the contents and others are acting as content producers. Based on these the relevance of the use-cases can vary significantly. Also, whether the distribution of the contents is via TV or OTT platform, the data available and the depth of AI/ML application can be different. Below are the emerging AI/ML Use-cases for Media & Entertainment Industry.

Meta Tagging to Extract Structured Data from Video & Audio Streams

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  1. Meta Tagging and Its role in Content Creation and Enabling Search for OTT Platform

Media industry has huge video content available with them. They are looking for ways to make it a powerhouse of insights and to enable this, we need to create structure data from the video contents. Meta Tagging is a process to take video content inputs and create structured meta data about the videos. This is done using Deep Learning frameworks that can help in extracting information such as actors, their roles, speakers at different segments, locations, type of scene, objects involved, genre etc. The meta data generated via deep learning and machine learning can be used as an input augmenting:

  • Search: when audience is looking for specific type of contents based on cast/actors, genre etc, the search can be enriched using the meta data created based on the AI/ML algorithms.
  • Content Recommendations: For audience engagement on the OTT, the meta data can be used an input to recommend relevant contents/video to the audience.
  1. Meta Tagging for User Insights

Typically, the viewership ratings (TRP) are available for the TV channels at every one minutes level. The rating can be linked back to the meta tags of each minute video contents to understand positive and negative engagement drivers for the contents. The meta tags (such as presence of characters/actors, speakers, location, type of scene etc) are generated from the video contents on air for the channels.

  1. Intelligent Sports Highlights

For the sports such as Cricket, Tennis, Football, a lot of audiences comes back and watch the highlights. These highlights can be automatically created based on the AI/ML Algorithms.  And further nuances can also be modelled such as in Crickets, we may want to see the catches, run-outs, sixes or fours etc. These highlights can be used both on the TV channels and on the OTT platform.

  1. Story Insights from the video episodes

There is huge interest in the TV serials in India and people do prefer to read about the upcoming episode for a serial. For example, on Star TV, Anupama is leading serial, and audiences are waiting to see the twists and turns of the upcoming episodes. The audiences can be engaged by sharing short article on the episode ahead. The article can be created based on the video contents of the episode using AI/ML. One approach could be to use audio transcript followed up by text summarization using NLP and creating title using the transcript text.  

  1. Content Review Intelligence

There are various regulatory and legal checks required for the video content before it goes on air. For example, in India, any film with smoking scene must show warning message on danger of using tobacco products. Similarly, a movie may have any old song that may require to be assessed for legal rights to include in the movie. These all can be automated effectively using AI/ML with minimized compliance issues. The model can detect smoking scene and whether warning has been included.