What is emotion analytics and why is it important?
In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.
Interactions, facts and feelings shape our relationships. A truism: It’s not what you say, but how you say it. Expressions matter, as do the sentiment behind each encounter and the emotions raised. Emotion is entwined with the literal meaning of words used.
This fact/feeling principle applies to both inter-personal and business relationships. “Emotional and factual appeals cannot be easily separated,” writes Nigel Hollis of Kantar Millward Brown in an analysis of advertising approaches. “[A] distinction between emotional and rational is one that exists only in the minds of marketers, not consumers,” according to Hollis.
The fact/feeling equation is central to corporate customer experience (CX) initiatives. CX practitioners map customer journeys that are defined by both the what and the how-did-it-make-you-feel? of customer-brand interactions. “Emotion drives loyalty,” according to CX visionary Bruce Temkin, and loyalty drives profit.
Another truism: You can’t improve what you don’t measure, not systematically, on a corporate scale.
Sentiment analysis and the varieties of emotion AI
Enter sentiment analysis, software technology that quantifies mood, attitude, opinion, and emotion in digital media, in images, video, audio, and text. One subspecies infers emotion via facial-expression analysis. Providers include Affectiva, CrowdEmotion, Eyeris, Kairos, nViso, Noldus Information Technology, RealEyes, and Sightcorp. Another variety analyses emotion in speech. Check out audEERING, Beyond Verbal, EMOspeech, Good Vibrations, NICE, Verint, and Vokaturi. On the text front, natural language processing (NLP) techniques can identify and extract emotion in online, social, and enterprise sources, delivered by companies that include Clarabridge, Crimson Hexagon, Feedback Ferret, IBM (AlchemyAPI and Watson Tone Analyzer), indico, Receptiviti, and an advisee of mine, Heartbeat AI Technologies.
This article aims to get a handle on the state of emotion analytics — specifically, emotion in text — via an interview with Heartbeat founder Lana Novikova. Lana describes herself as a marketer by training and a market researcher by trade, never satisfied with numbers and observations, always pushing to understand the “deep why” behind consumer (and her own) decisions. She’ll be presenting at LT-Accelerate, a conference I organise, November 21-22 in Brussels, alongside Odile Jagsch, a consultant at global market research consultancy Kantar TNS, topic “The ‘Why’ Behind Customer Loyalty.”
Seth Grimes. Heartbeat designs “emotionally intelligent technologies.” OK, what’s an “emotionally intelligent technology”?
Lana Novikova. Let’s start with the concept of Emotional Intelligence (EQ), popularised by a psychologist Daniel Goleman in 1990s.
Imagine a newborn human who comes with a basic wiring for recognising and expressing key emotions, and with an enormous capacity to learn. She can cry or stay calm, smell and turn her head towards her mother’s breast. Once she can see faces, her mirror neutrons start learning and mimicking facial expressions; then she develops more and more capacity to read and express emotions — from touch, to tone & voice recognition, to basic language to more complex if-then scenarios. In a perfect world, she grows into a secure and happy person who can recognize and name a wide range of her own emotions, understands what other people feel from multiple expressions, and has a capacity to express and manage her emotions.
SG. So you apply the EQ concept to and via technology.
One day, this technology might surpass humans in understanding human emotions because it will tap into data that humans can not perceive on their own.
LN. Technology today has a super high IQ — it can beat the best human chess, Jeopardy, and Go players — yet it has a very low EQ. At Heartbeat, we want to be a part of an academic and business community that changes this. Emotionally intelligent technology is never going to feel emotions or express them like our baby can, but it will eventually become very good at perceiving and understanding human emotions from data.
One day, this technology might surpass humans in understanding human emotions because it will tap into data that humans can not perceive on their own: biometrics, brain waves, subtle cues from body language and facial expressions, and more.
SG. Relating the tech to Heartbeat…
LN. We are focusing on training the (metaphorical) technology infant to recognize explicit feelings from language, from text, and to guess the range of emotions it communicates. Just as some people can intuitively differentiate between many emotions, our growing algorithm can tell what kind of Joy or Anger is expressed in language. There could be as little as 2-3% and as much as 50% affect words and phrases in any given unstructured text. We find these words and assign them to a cluster of emotions. This process mimics how our brain deciphers emotions from language.
