Emotions can be powerful for individuals. But they’re also powerful tools for content creators, such as advertisers, marketers, and filmmakers. By tracking people’s negative or positive feelings toward ads — via traditional surveys and focus groups — agencies can tweak and tailor their content to better satisfy consumers.

Increasingly, over the past several years, companies developing emotion-recognition technology — which gauges subconscious emotions by analyzing facial cues — have aided agencies on that front.

Prominent among these companies is MIT spinout Affectiva, whose advanced emotion-tracking software, called Affdex, is based on years of MIT Media Lab research. Today, the startup is attracting some big-name clients, including Kellogg and Unilever.

Backed by more than $20 million in funding, the startup — which has amassed a vast facial-expression database — is also setting its sights on a “mood-aware” Internet that reads a user’s emotions to shape content. This could lead, for example, to more relevant online ads, as well as enhanced gaming and online-learning experiences.

“The broad goal is to become the emotion layer of the Internet,” says Affectiva co-founder Rana el Kaliouby, a former MIT postdoc who invented the technology. “We believe there’s an opportunity to sit between any human-to-computer, or human-to-human interaction point, capture data, and use it to enrich the user experience.”


In using Affdex, Affectiva recruits participants to watch advertisements in front of their computer webcams, tablets, and smartphones. Machine learning algorithms track facial cues, focusing prominently on the eyes, eyebrows, and mouth. A smile, for instance, would mean the corners of the lips curl upward and outward, teeth flash, and the skin around their eyes wrinkles.

Affdex then infers the viewer’s emotions — such as enjoyment, surprise, anger, disgust, or confusion — and pushes the data to a cloud server, where Affdex aggregates the results from all the facial videos (sometimes hundreds), which it publishes on a dashboard.

But determining whether a person “likes” or “dislikes” an advertisement takes advanced analytics. Importantly, the software looks for “hooking” the viewers in the first third of an advertisement, by noting increased attention and focus, signaled in part by less fidgeting and fixated gazes.

Smiles can indicate that a commercial designed to be humorous is, indeed, funny. But if a smirk — subtle, asymmetric lip curls, separate from smiles — comes at a moment when information appears on the screen, it may indicate skepticism or doubt.

A furrowed brow may signal confusion or cognitive overload. “Sometimes that’s by design: You want people to be confused, before you resolve the problem. But if the furrowed brow persists throughout the ad, and is not resolved by end, that’s a red flag,” el Kaliouby says.

Affectiva has been working with advertisers to optimize their marketing content for a couple of years. In a recent case study with Mars, for example, Affectiva found that the client’s chocolate ads elicited the highest emotional engagement, while its food ads elicited the least, helping predict short-term sales of these products.

In that study, some 1,500 participants from the United States and Europe viewed more than 200 ads to track their emotional responses, which were tied to the sales volume for different product lines. These results were combined with a survey to increase the accuracy of predicting sales volume.

“Clients usually take these responses and edit the ad, maybe make it shorter, maybe change around the brand reveal,” el Kaliouby says. “With Affdex, you see on a moment-by-moment basis, who’s really engaged with ad, and what’s working and what’s not.”

This year, the startup released a developer kit for mobile app designers. Still in their early stages, some of the apps are designed for entertainment, such as people submitting “selfies” to analyze their moods and sharing them across social media.

Still others could help children with autism better interact, el Kaliouby says — such as games that make people match facial cues with emotions. “This would focus on pragmatic training, helping these kids understand the meaning of different facial expressions and how to express their own,” she says.

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