Machine Learning Applications in Media

Nowadays, Media is one of the most powerful and influencing means in the entire world, and also the applications of media have rapidly increased in past decades. It is the term that includes all print, digital, and electronic means of communication. Content creation is one of the most important factors that is witnessing how the media industry is continuously transforming and facing more competition in a market that is driving the need to reduce operating costs and simultaneously generate more revenue from delivering content. With the evolution in the media industry, the use of Machine Learning and Artificial intelligence technologies has also been increased to a great extent. AI and ML help the media industry in various ways, such as making visual content more interactive, interesting, user friendly, and improving efficiency as well.

In this topic, "Machine Learning Applications in Media", we will discuss various machine learning applications that become essential for the media and entertainment industry and growing businesses with more profit and revenue. So, let's start with a quick introduction to machine learning in the media industry and some popular machine learning applications required for the media & entertainment industry.

Machine Learning in the Media industry

The media and entertainment industry perceives exponential growth globally. With the use of ML-enabled high-speed network systems and trending video streaming platforms, the users are accessing unlimited content continuously without any interruption.

As per the information published by Statista, the value of the global entertainment and media market from 2011 to 2025 has increased to a great extent.

As per the reports, the value of the worldwide entertainment and media market fell to two trillion U.S. dollars in the year 2020. However, the forecast for 2021 suggests revenue will begin once more rise and surpass the pre-COVID levels, with a 2.2 trillion dollars result. The rapid growth in the media industry is primarily because most people are using an online platform like YouTube, Facebook, and Netflix instead of classical channels such as cable and radio FM.

Machine Learning (ML) applications in Media

The applications of Machine Learning are live witnesses for rapid growth in the media industry in different forms such as showing & distributing visual content, audio content (2-D and 3-D), digital advertisement, product recommendation, target audiences, content classification, and categorization, meta-tagging, automated transcription, virtual personal chatbots, detecting and removing false information, or sentimental analysis, etc.

There are a few important applications of machine learning in media with their example. These are as follows:

• Content personalization and Recommendations
  Companies are offering services to their customers so that users can personalize the audio and video content as per their preferences and previous experiences. All big companies like YouTube, Netflix, Spotify, etc., are providing this feature to make their services more reliable and user-friendly.
  Machine Learning helps in collecting users' data, behaviors, and demographic details and accordingly recommends them for appropriate content that they liked most in the past. Various ML algorithms and deep learning methods help in delivering more personalized content to users. In this way, companies are using ML technologies to increase their customers with improved customer service experience than other competitor companies in the market.
  For e.g., Netflix is USA based application that provides various entertainment services to the user. If you like action web series and in the past, you have more searched similar types of movies, then Netflix will automatically recommend some other similar series as per your interest. On the other hand, while shopping on Amazon, it automatically recommends users more similar products as per their interest.
• Digital Advertisement and Target audience
  Digital advertisement is one of the easiest methods to drive revenue and promote your business online. It plays a significant role in branding and business promotion. Machine learning (ML) technologies help significantly in making digital advertisements more precise and productive. Further, it also helps in building a target audience with higher conversion rates. Conversion rate is the term that tells how many users have purchased products/services through advertisements on your platforms.
  Let's understand one of the most popular examples of Google AdSense that helps in showing advertisements based on users' past history and preferences. If a user has searched for Apple iPhone in their web browser or eCommerce sites, then it gets started to show similar category products on different websites. Hence, Google also uses AI and ML technologies to help advertisers in targeting the right audience and get maximum outputs from the Ads.
• Content classification and categorization:
  Content classification and categorization based on user preference is one of the important goals of media and entertainment platforms like YouTube, Amazon Prime, OTT, etc. These platforms have different genres of music videos, songs, movies, or web series through using various ML algorithms. Implementing ML technologies and algorithms in media and entertainment sectors can automate the categorization and classification of content for developing a better user-friendly environment.
• Meta Tagging Subtitles & Automated Transcription:
  Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.
  Further, apart from that, movies, music videos, and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is dubbed into various different languages with subtitles and audio annotations to generate more customers globally.
• Personal virtual chatbots
  Every business requires a personal virtual assistant to assist their customer in solving their queries remotely. Machine learning and Artificial intelligence play a vital role in training and developing virtual chatbots for the media and entertainment industry and improving efficiency as well. That eventually helps these companies in offering better services to their customers.
• Identifying fake information
  Now a day, there is so much fake news, and posts get viral on social media or other platforms. Such fake news provokes the audience towards certain events or social issues. ML-based technologies help to identify and report such content and remove them before circulation.
  Further apart from text content, some of the users also create fake videos or edited videos using deep fake technology. But with the help of ML and AI deepfake detection services, these videos and images can be detected, removed, and can be reported. Moreover, we can notify the platform owner to take appropriate action on such things so that no one can do the same things again in the future.
• Using social media for Sentiment Analysis
  Sentimental analysis is defined as the techniques used by various organizations to analyze the content published on social media sites. This published data can be collected and again used by machine learning to develop ML models that can analyze the sentiments and feelings of the people interacting with each other on social media platforms.
  For e.g., Facebook is the world's largest social media platform which provides a free space to share views, content, and opinion on different topics. Analyzing such discussion of different age groups or people from different regions or demographics provides useful insights about the different people.
• Reporting automation
  AI and ML technologies are used to automate the company's business as well as help them to make strategic business decisions. All big media platforms use natural language processing and machine learning technologies to generate channel performance reports from raw information shared by regulatory authorities. This information was received in the form of large excel sheets. Analyzing these excel sheets once a week proves that it is very difficult for the analysis team to generate and implement meaningful exercises.
• Streaming Quality
  AI video enhancement software can help media and entertainment channels; for example, Netflix improves the video quality, and lets devices consume fewer mobile data required to stream. Netflix has upgraded its code by embedding ML algorithms for improving streaming quality.
• Search optimization:
  The main goal of each audience is to find appropriate content available over the internet. Sometimes, it becomes really tough to find what we exactly require. AI and ML help to make search results more similar and accurate as per the user's requirement. Search optimization is one of the best and most popular ML applications used for the media industry.

Conclusion

By this topic, we have understood how AI and ML are useful for the media and entertainment industries. Each media and entertainment industry is using AI/ML applications to enhance their business and maximize profit. Big data also helps AI and ML to provide a huge amount of data for training ML models because machine learning needs a vast amount of data to train their models. The more effective data an ML model will take, the more efficient result it will generate.