How Video AI Is Changing Social Media Listening

Social media listening allows marketers to gather valuable consumer insights from social media platforms. It’s not a surprise then that brands are using video platforms like TikTok, more and more, to amplify their presence. Encouraging customers to leave comments on posts and user-generated videos is a very smart tactic that can give you deep insights into how they perceive not only your brand but also competitor brands. This information is gold. But the key to capturing this critical information from social media data is using a tool that is driven by video artificial intelligence or video AI.

Table of contents:

• Re-inventing Social Media Listening With Video AI
• Why Do We Need Video AI in Social Media Listening?
• How is Social Media Listening Done Using Video AI?
• Conclusion

Re-inventing Social Media Listening With Video AI

Savvy businesses know that the time is ripe to go beyond traditional BTL (below the line) and ATL (above the line) marketing techniques and capture a burgeoning market ready to be leveraged using the more hip, casual medium of social media. To understand why, one need just look at how Twitter alone has more than 2 billion video views each day.

Similarly, Facebook’s video platform gets more than 75 million hits on a daily basis. As customers go, around 60% of people say that they are more interested in a product after seeing it on a Facebook video ad. That’s why social listening tools that mainly analyze text are no longer good enough. They need to be re-invented and boosted with video AI.

Look at TikTok. It’s the rage on the social scene with user-generated video content. So much so that businesses are tapping aggressively into TikTok to reach a wider consumer base to attract GenX audiences.

Further to this, social media is an entity in its own right, with its unique lingo and user-behavior patterns. Social media listening needs to not only incorporate code switches, social abbreviations, user patterns, and emojis, but also analyze them through aspect-based sentiment analysis so that the complex emotions expressed can be broken down into sections, and the sentiment for each aspect of a business or product extracted for study. See this complex comment as an example.

Video AI allows you to tap into video content in such a way that even if you have videos with comments turned off, you can still analyze the content for insights. This is vital if you want insights into certain hashtags, keywords, user profiles, competitor brands, influencers, and other criteria. We talk about this in detail in the next section.

Why Do We Need Video AI in Social Media Listening?

As people share, create, and voice their opinions in video content to a greater degree, social media listening requires tools that are able to analyze video content and extract insights from it. Otherwise, you defeat the whole purpose of learning from your social media analytics data.

A look at the top channels that you need to review for social media listening itself tells you why video AI is changing the way we do social media listening.

• TikTok: TikTok has grown to become one of the most popular social media platforms. And it’s understandable. It’s an excellent marketing tool across a variety of industries. As a result, there’s a wealth of insights you can gain from the platform, provided, of course, that you have the right video content analysis tool that can extract insights from the video content as well as text captions.

• YouTube: YouTube is arguably the largest and most popular video-sharing platform available, both for shorter content and longer, in-depth videos. It streams more than 1 billion hours of video on a daily basis. Video AI lets you extract vital consumer information not only from these videos themselves but also from the comments. You can also compare sentiment scores for each video and see patterns emerging that you can use in your marketing strategy.

• Instagram: Originally conceived as an image-sharing platform, Instagram is now focussing its efforts on videos. IGTV is very popular amongst millennials as well as Gen X. With the right sentiment analysis and social media listening tool, you can unlock crucial factors along with user demographics, which will allow you to measure and monitor brand awareness and consumer trends and plan your social strategy better.

• Facebook: Facebook is simply one of, if not, the largest social media platforms available. Numerous businesses use it to generate awareness about their brands, products, or services. Likewise, with its massive user-base, customers too use Facebook to air their opinions about brands and how they really feel about products or services. This enables you to get more in-depth feedback from your customers than ever before. It also helps you engage with them more effectively and proactively.

No matter which platforms you use to reach your audience, video AI in social media listening helps you gather cross-platform intelligence that will allow you to understand your customers better and meet their needs and expectations. You can plan and create your video content better and be able to optimize your content strategy to attract more customers and grow your audience.

Ultimately, this means that you can reach out to your market more effectively, sell more products or services, and generate more revenue.

How is Social Media Listening Done Using Video AI?

When social media listening is equipped with video AI, its sentiment analysis API runs on machine learning algorithms and natural language processing tasks, analyzing video content as easily as text data. It does through the following:

Audio Transcription: All the audio streams in the social videos are converted into text using speech-to-text software. supported by neural networks.

Caption Overlay: The tool extracts any text that is contained in captions. It will analyze the video on a frame-by-frame basis so as to identify and analyze any text appearing in these image frames.

A good example of this is a video that discusses a specific product and has captions containing the advantages and disadvantages of using the product, the audio necessarily does not verbally mention these pros and cons. Here, extracting the text from captions will enable you to gather insights that are not there in the audio stream.

Image Recognition: The video AI tool will identify any images in the video. This is an extremely helpful way to identify products or other brand logos appearing in the video.

Text analysis from posts and comments: The tool extracts and processes the text contained in the comments accompanying the video. In addition, the text analytics API also recognizes and extracts common social media jargon, hashtags, and emojis.

This is one of the most important components of the social media listening process because the discussions surrounding a video are often when you’ll find the most valuable insights. For instance, these discussions will give you insights into your customers’ opinions not only about your brand or product but also those of your competitors.

Sentiment Analysis: Ultimately, the sentiment analysis API will analyze all the data for user sentiment. It will use machine learning tasks such as natural language processing to recognize and categorize themes, topics, and aspects in the text and then assign a sentiment score to each of these themes or topics.

These scores range between 1 and -1 depending on the specific sentiment of the topic in question. You can view all these insights on a visualization dashboard and use the data to plan your next steps and strategies.

Conclusion

In a video-centric marketing landscape, it’s a no-brainer that you can’t really generate value from social media listening without video AI. As an increasing number of social media users turn to video content to share how they feel and a social media listening tool equipped with video AI gives you the power to reach out and grasp this market.

This trend is just the beginning, as platforms explore clever ways to entice more content creators. This is good news for brands and their marketing teams. All you need to do is ride the wave and make sure you are leveraging machine learning to do the heavy lifting.

Martin Ostrovsky is the founder and CEO of Repustate. He is passionate about AI, ML, and NLP. He sets the strategy, roadmap, and feature definition for Repustate’s Global Text Analytics API, Sentiment Analysis and Named Entity Recognition.