Repustate's video analysis treats your audio and video content as if it was a plain text document. Extract sentiment, topics and themes from all of your video content and make it searchable with Repustate's semantic search.
Book a 15-minute demoRepustate stood out as the best solution for us. They fully understood our use case, both in terms of immediate needs and long-term vision.
We were blown away by the fact that they were able to put together a demo using our own YouTube channels on just a couple of days notice. What really stood out was the built-in semantic search capability.
Semantic video content analysis (VCA) is an AI-based technique used to extract the main thematic message from videos. It includes tasks such as face detection, template matching, caption extraction, moving object segmentation, and more. It allows for sentiment analysis of video content for customer opinion, product insights, and brand amplification. Video analysis AI is used in entertainment, sports, video search, security, traffic management, and many other fields.
Repustate’s powerful machine learning (ML) video content analysis solution uses natural language processing (NLP) to extract insights from videos. It can semantically archive sales and presentation videos, as easily as it can gather consumer insights through YouTube sentiment analysis or even Tik Tok sentiment analysis. The process below shows how semantic video analysis is done.
Step 1. Audio transcription
All audio and video files are converted into text using speech-to-text backed by neural networks (NN). This means not only can Repustate analyze videos but also podcasts and other voice recordings.
Step 2. Caption overlay
Videos are broken down into frame-by-frame image formats. Any text that appears in these frames is recognized and extracted.
Step 3. Image recognition
Through its video analysis AI, the model recognizes any images present in the background. Brand names and logos are detected as well and classified as entities.
Step 4. Text from post & comments
Any comments accompanying the video content are ingested and processed using Repustate's Text analytics API. The text analytics pipeline recognizes hashtags, emojis, and other common social media colloquialisms.
Step 5. Sentiment & semantic analysis
With all relevant text now extracted, both sentiment analysis and semantic analysis are used to extract key topics and themes as well as their relative sentiment.
Step 6. Visualisation
All the sentiment scores are then projected onto a text analytics dashboard so that you can easily understand the data that has been analyzed.
The ability to access, analyze, and (re) use large amounts of data is of vital importance. There is a continuously growing sea of user-generated video content strewn across social media platforms along with video big data accumulating across enterprises. The challenge is to have a video analysis platform that can not only annotate and organize semantically for video search, but also harness sentiment scores for customer insights from Voice of the Customer data and social media listening.
With VCA, you don’t have to rely on the traditional and time-consuming manual process of reviewing videos for relevant topics, themes, and events. Not only is manual intervention mired in extensive costs but also errors due to human bias and inconsistencies. Repustate's impressive video content analysis solution offers a simpler and more sophisticated way of semantic video analysis through image recognition and sentiment extraction of videos on YouTube, IGTV, Facebook, and even TikTok.
Repustate’s AI-driven video content analytics model works in 23 languages and dialects. You can extract sentiment and brand mentions from videos on YouTube, Facebook, Instagram, TikTok, and any other major channel. You can also dive into podcasts and enterprise-wide audio files. Repustate’s intuitive video content analysis API can also identify logos, for the most thorough analysis of video data. All of these insights are displayed in an easy-to-use and intuitive dashboard. The unique features of Repustate’s video content analytics are:
Semantic video analysis is used in fields as varied as security and surveillance, news monitoring, teaching hospitals, market research, gaming platforms, sports broadcasting, and other dynamic industries. Not only does semantic analysis of videos help in the deeper understanding of customers but also in improving the delivery of functionalities each industry offers. The most common applications of Repustate's video content analytics are:
Learn more about applications of video analytics.
Video content analysis helps companies evaluate brand sentiment by drawing customer insights from video data. Through video sentiment analysis, it helps businesses focus their resources better for product enhancement and improved customer care. Below are are some ways in which VCA provides key assistance in extracting Customer insights:
Nowadays, it's necessary to gauge how people feel about your product or service. Just like Facebook and Twitter, many people take to YouTube to voice their opinions on a brand. With YouTube Sentiment Analysis, take your Text Analytics and Sentiment Analysis to the next level and gain remarkable insights on what your customers are saying about you and what they are looking for from your brand.
Repustate's video content analysis gives you a peek inside the minds of your audience by really understanding their preferences. Having your video content analyzed semantically allows you to extract Voice of the Customer and pinpoint accurate insights into what content resonates with your audience and what doesn't.