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Video Content Search: What, Why & How

Video content search allows users to extract text in speech, themes, logos, images, and other metadata in video libraries for quick understanding and indexing of the content. In this article, we read about how search inside video works, and the kinds of advantages it offers to a variety of businesses and industries.

Video content search is a machine learning task that identifies topics, thematic elements, categories, and subject matters from within your entire video repository and gives you the insights you want. This capability to search inside video data goes beyond text and includes captions, logos, images, tags, technical details, and much more. This is important because video content can be from any source like social media, news monitoring, reviews, etc. As a result, video AI allows for voice of the customer analysis as easily as helping video platforms like Vimeo, ArtGrid, or Shutterstock manage their content.

With an automated system at your hand, you can get to the exact point in a video that is of relevance to you not only through keyword search but also through semantic search that does not need the exact keyword. Video content search platforms offer you dynamic suggestions of timestamps to choose from so that your search is easier and quicker. They can also help businesses contextualize their video data for easier categorizations. All this without you ever having to manually edit or scan your database.

What Data Can Be Searched Inside Videos?

You can search inside videos using key searches like titles of videos, themes you are looking for, categories, descriptions, and many more criteria as are discussed in detail below.

  • Titles

Video content search can be used for video repositories in general, or when you are exploring customer experience data and want to obtain TikTok insights from social media videos. You can search for the complete title or just key entities or words in the title. The software automatically removes common words and prepositions like “the”, “a”, etc, while searching and gives you results also based on semantically similar Titles in case you don’t know the exact name of the video.

  • Themes and aspects of movies

This is a very useful feature that is used by video streaming channels like Amazon Prime, Netflix, or even DailyMotion. But it is also very relevant in primary and elementary education, where children are introduced to new concepts and ideas through audio-visual formats. Learn how Repustate helped the Ministry of Education in Egypt with its complex academic repository.

  • Categories

Search inside videos can be applied to categories in the video data. The algorithm scans all the videos and extracts similarities in themes and metadata and segments them into categories based on these aspects. Thus, you will be able to search for videos in a library, for example, based on categories like high school mathematics, geography, nautical science, fiction, documentaries. In a business context, you can search for company videos by categories of campaign names or departments.

  • Descriptions

Video descriptions are important not only for search engine optimizations for digital marketing but also for search functions within an organization’s video database. In a teaching hospital, for example, you can search for topics like waste management, dental surgery for molars, or open-heart surgeries conducted by particular hospitals or surgeons. You can also search for patient education videos that have been found to be very effective in patient management and outcome.

  • Tags

Video content search can be used for discovering videos based on tags that have been given to them already based on context and relevance. You can search inside videos based on these tags and receive snippets of videos with exact timestamps as you narrow your search further through keywords.

  • Entities and mentions

Video AI platforms ensure that when the algorithms scan and analyze a video repository, they discover and extract entities based on the training the model has been given. These entities are industry-driven and sometimes can have overlaps. You can get multiple results, all matching the entities you have provided, which can be names, mentions, locations, brand names, corporate events, etc.

  • Text in speech

Natural language processing (NLP) tasks identify and convert to text all that is said in the audio-video formats of your data and so allow you to search inside videos based directly on the content of the video. Even if there is no tag or category applied for you during your search, but you do remember certain talking points of the video, the platform will search for the specific video using semantics and NLP based on the speech-driven contents of the video.

  • Technical data

Video content search can also work on technical data that is either present in the videos themselves, or in the backend. This means you can search inside videos based on the original source of the video. This is especially useful when you do not know the specifics of the video or want to have an overview of all data from a particular source, for example, a particular social media channel. Metadata, file size, archived data, all can be attributes that you can search for to find the video sets you require.

Video content search can help companies tremendously in knowledge management within enterprises, academic and non-academic libraries, smart device applications, editing suite platforms, video streaming services, podcasting, research, and much more. We explore these advantages in the following points:

1. Enterprise Knowledge Management

Enterprise knowledge management is a very crucial function in an organization. A company’s data repository holds all kinds of important information - from financial data to information pertaining to human resources. Similarly, other information like customer data, patient voice data, intra-organizational video and audio data, all need to be organized cohesively in a seamless and integrated manner. Video content search helps do this effectively and efficiently.

2. Academic Archiving

Libraries, universities, colleges, elementary schools, teaching hospitals, and other educational institutions, have vast and diverse data in both video and audio formats. Video content search is extremely useful for these applications because all library data needs to be semantically organized data, which includes image, audio, and video content.

3. Business Directories

All local and international governing bodies, trade boards, and chambers of commerce have business directories. These directories serve as a connecting point for members as well as clients. Search inside video helps in a contextual organization of directory data that can also include company information, portfolio, and other member information.

4. Marketing Campaigns

Video content search helps companies manage, organize, and retrieve important campaign data. This is very useful for market outreach based on different customer demographics, geolocations, e-commerce ventures, brand ambassadors, cost-of-production, and other such areas. When marketing agencies use this in conjunction with a sentiment analysis API they can get valuable insights that would otherwise remain hidden in all the data.

5. Smart Devices

Search inside video is particularly useful when you are using applications for smart home devices, phones, tablets, and other integrated systems that store information based on your usage. You can retrieve information based on keywords, as well as contextual information.

6. Editing suite platforms

Video content search is an invaluable tool in the hands of editing platforms like Adobe Premiere, Pinnacle Studio, Filmora, etc. Because video AI enables archiving, organizing, and search of data based on graphics, angles, chronology, sources, etc, you can seamlessly locate the necessary data in these editing tools.

7. News monitoring

Video content analysis and search inside video help in news monitoring by making sure that all new items including videos are analyzed while gathering insights from news websites. Video content search can help such organizations to search news by trends, current affairs, regional news, industry domains, and any other criteria.

8. Video libraries

Search inside videos is a very helpful function for video libraries like Oberlo, Netflix, Vimeo, Shutterstock, and such because they allow users to search videos by categories, themes, events, dates, etc. Video AI ensures that these videos are organized for easy consumption and retrieval.

9. Research

Whether it’s IDG, Forrester, Ph.D. students, data scientists, financial analysts, or marketing teams, video content search is a boon for anyone looking for varied information across domains and industries. From competitive analysis reports to movie clips, search inside video gives you timestamps of videos that may be of relevance to you, so you don’t have to manually scan entire libraries nor the video itself in its entirety.

10. Podcasting

With the popularity of podcasts at its very peak as we know it, search inside video is a very important tool that not only thematically archives audio files but also does it at scale. With the help of semantic audio search, companies, as well as individuals, can use podcast data to discover insights on a multitude of topics.

Discover more: Importance of Video Analysis

How Is Video Content Search Done?

An AI-based machine learning model conducts video content search in 4 steps. These are:

Step 1: Speech to Text conversion

The model converts all the audio in the video data to text using speech-to-text software. Every language is read and analyzed natively using its own speech tagger for natural language processing tasks.

Step 2: Text indexing

The machine model now indexes the text data. The video data is segmented into various items based on parameters such as background scenery, pauses, the person in focus, the speaker, etc. As this data is analyzed thoroughly, the model recognizes and extracts entities, keywords, and other vital information.

Step 3: Logo and Image Detection

In this step, video AI discovers and identifies logos. These are annotated and labeled just as other text is. This is a unique function in video content search because the machine learning algorithm uses neural networks to coordinate pattern recognition and understands the relationship between words that can be entities, locations, people, objects, and so forth, and the user’s past search history. Knowledge graphs help in this stage because they are armed with a humongous amount of data that tells you the context of a keyword, thus helping in the semantic search of video data.

Step 4: Result extraction

Once all the above steps are done, video timestamps are created, and searched items can be itemized based on themes, metadata, topics, or entities found in the videos. When a user applies search inside video, the result will get them snippets in all matching videos from the repository. If using the videos for emotion mining for customer experience analysis, you can see all relevant insights from the videos on a sentiment analysis dashboard, which is integrated with the platform.

Boost Your Efficiency With Video AI

Video content search gives organizations the benefits of time and reduced costs since automated intelligent search can replace error-prone and time-consuming manual management and retrieval of video data. Enterprises have already begun using advanced custom-built applications of video AI for improved functional abilities. In fact, Repustate’s video content analysis platform has helped numerous clients across the globe in 23 languages in fields as diverse as education, healthcare, research and surveys, and more.