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AI Tools Help CBS Get a Quicker Read on Viewer Emotions

Cbs AI Test Tv Content Emotional Responses

CBS’s research team is getting a quicker read on viewer responses to TV shows by using the dispassionate logic of machines. The broadcaster is using New York startup Canvs to automate the coding of open-ended comments in surveys it fields to audiences for its tentpole events and programming.

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What is CBS AI test

A New York startup is using AI to crunch open-ended responses to surveys about CBS TV shows, allowing the network to get a read on audience emotions more quickly than its research team can. CBS researchers then share the results with creative execs, who can use the data to make informed decisions about future content. For example, if viewers express deep passion for one character on a show, the creative team may choose to give that character more screen time in future episodes.

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Does CBS AI test analyze emotions

CBS is using an artificial intelligence tool to analyze viewer responses to its TV shows and tentpole events. The company has partnered with Canvs, a New York startup that uses AI to parse natural-language comments. The technology can quickly identify emotional responses to TV shows, enabling CBS to make more accurate programming decisions.

The tool also identifies the most emotionally engaging moments in longer narratives, helping broadcasters and movie studios optimize content. It can also detect when viewers are confused or unengaged, which can help producers improve the story arch.

Another artificial emotion recognition technology is Affectiva, which uses computer vision and AI to measure how people react to different marketing campaigns. The software analyzes a person’s facial expressions and determines what type of emotion they are feeling.

What are emotional responses to TV

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Emotional responses to TV content are evoked by characters, drama, tension and other factors. They can be captured using a variety of methods, from asking open-ended questions in surveys to conducting qualitative interviews and dial testing. There are also a number of technological techniques that allow researchers to observe and measure emotions without questioning participants. However, these methods can be costly and intrusive. Therefore, researchers have also developed heuristics to assess emotional responses to TV content.

Research on emotions generally endorses either dimensionality or classification paradigms. The dimensional view emphasizes the generality of emotions and their relationships. The cluster analysis paradigm, on the other hand, emphasizes the importance of distinguishing between different aspects of a given emotion. In this study, a brief checklist questionnaire was tested for its replicability and extension to assessing emotions evoked by TV ads. The results indicate that aggregation of the items into eight scales can describe the emotional impact of the ads and confirms the circumplex model of affect.

It also revealed that both classificatory and dimensional analyses yielded interpretable groups of emotions while two-dimensional solutions parsimoniously described the relationships among these groups. Moreover, ad likeability was found to be higher for ads that were previously seen than for first-time-seen ones.

Can AI analyze emotional responses

Using its proprietary artificial-intelligence processing to parse natural-language comments, New York startup Canvs has created a tool called Surveys that lets researchers apply emotional-response analytics to their own data sets. The company’s lexicon covers 1 trillion words, phrases, slang terms and idioms and maps them to standardized emotion categories.

The technology can pinpoint viewer confusion, as well as the most emotionally engaging moments in long narratives, so creative execs can retain and optimize the best parts of a show or film. It can also identify the key characters that drive audience engagement and help marketeers understand the impact of trailers or marketing content.

What is the purpose of CBS AI test

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The research team at CBS is getting a much faster read on how viewers respond to its TV shows by deploying an AI-based tool called Canvs. The platform uses machine learning to automatically code open-ended responses to surveys fielded by the network’s research team.

The team is also using Canvs to help identify the most emotionally engaging moments in longer narratives, which can help them optimize content and cut down on editing time. It can help determine if viewers are engaged with characters on TV shows or films and how well they interact.