Predicting 2016 Elections

The Role of Emotions in Decision-Making

While people’s brains differ in how quickly they make decisions, or how confident they are about them, all modern science, from behavioural economics to affective neuroscience, agrees that emotions are essential in decision making. Humans are rational and emotional, and our moment-by-moment subjective sense of wellbeing influences our decisions.

If both thoughts and feelings COMBINED drive decision making, how do we measure this complex process?

In a changing world affected by realities such as climate emergency, automation, and an aging population it is increasingly important to understand and measure the collective emotions and feelings which heavily influence some of the most vital decisions in history — such as elections.

This case study illustrates the value of integrating emotion-based metrics in quantitative approaches. Excitingly, it demonstrates the improved predictive accuracy we can achieve when emotion-based measures are taken into account.

Adding the “secret sauce” of emotion text analytics improved the accuracy of a predictive model and uncovered the WHY behind voters’ decisions.

It shows how measuring emotions helps us to better understand why voters behave the way they do. Understanding the WHY could allow candidates to find better ways create and communicate their platforms, perhaps based on different measurements of success such as empathy, trust, and the wellbeing of their constituents. This would increase candidates’ likelihood of success and help create a stronger, more just society.

The Standard Code of Emotional Language

A study by Cornell University neuroscientist Adam Anderson shows that although feelings are personal and subjective, the human brain turns them into a standard code that objectively represents emotions across different senses, situations and even people. Anderson writes: “Despite how personal our feelings feel, the evidence suggests our brains use a standard code to speak the same emotional language.”

Emotion text analytics is a relatively new area of analytics and an exciting method for measuring emotions. Based on Anderson’s findings that all human brains generate a “standard code” for the entire valence spectrum from good to bad, and express it accurately using language, we can rely on emotion text analytics to improve the accuracy of predicting future events that are based on human decisions, such as elections, as well as consumer and shopper decisions.

Uncovering The Emotions Behind Voter Decision-Making

In the Spring of 2016, Heartbeat AI partnered with Lewers Research (Australia) to discover the emotions of Australians prior to the National elections in order to predict the election outcomes.  The experiment worked better than we ever dreamed, so we repeated it using the same approach a few months later in the United States.

We used a combination of closed-ended and open-ended questions — an easy method to collect quality data related to people’s emotions.

Traditional or “rational” questions

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Open-ended or “emotional” questions

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Both studies were carried out in the same manner

The Google Survey Platform was used to collect data over a 3-month period leading up to election day.

  1. An A/B testing approach of the two leading candidates was used to gather rational and emotional data about each candidate.

  2. The open-ended responses were processed by the Heartbeat software to identify the primary and secondary emotions embedded within the context of the respondent’s answer. These binary variables were then weighted based on the combined factors of age, gender and region.

  3. A Random Forest methodology was used to build and train the model.

  4. We built three models: “Rational” (based on the three traditional questions), “Emotional” (based on the two open-ended questions with text converted into emotional variables), and a “Combined” model.

Three models were built in isolation to understand the predictive accuracy of the rational and emotional drivers on their own.

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The “Combined” Model predicted the election outcomes at the National level

The 2016 Election was the closest race in Australian history since the 1940s, with a margin of just 0.72%. The “Combined” model was successful in predicting the results on both the National and Regional levels.

Australian Election: Actual and Predicted Results

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The top 5 variables of the emotional model helped explain why Malcolm Turnbull won the election. While Joy and Anger played a similar role in voters’ preference, the differentiating factor was Trust. Being the incumbent, Turnbull had established a level of trust among Australians which provided sufficient advantage to win the election by the narrowest of margins.

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The “Combined” Model predicted the election outcomes for each of the four swing states we identified

We used the same approach for the US Election model, but instead of focusing on predicting the results on the National level, we identified four swing states: Florida, Iowa, Ohio and Pennsylvania (because they were the most challenging states to predict). As with the Australian model, the strongest accuracy comes from using the “Combined” model.

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Pollsters in this election were confident in their predictions of a Clinton win. And using only rational drivers, Clinton did appear as the stronger candidate based on experience, demeanour, political know-how, etc.

But once again, emotional drivers helped to explain how Trump won this election.

Predictably Predictable Emotions

The top five variables of Joy, Anger, Fear, and Trust within the emotional model clearly help to explain the role of voter emotions in selecting a candidate.

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The Heartbeat algorithm identified voters’ secondary emotions and discovered their powerful influence on voters’ choice. Secondary emotions provided insight into why Trump was successful in the four key states polled.

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For Trump, Anger was more aligned with the standard type of criticism directed at politicians.

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For Clinton, the emotions were stronger and more negative, with resentment being the strongest.

AI Asia: Canadian Startup Heartbeat AI Finds Success in Japan and South Korea

AI Asia: Canadian Startup Heartbeat AI Finds Success in Japan and South Korea

Lana Novikova is the Founder & CEO of Heartbeat AI, a Toronto-based AI startup that has developed a platform that helps users better understand the emotions of their clients and employees to make the best decisions. In this interview, Lana shares her experiences and insights on the field of AI and Canadian opportunities for this sector in Asian markets.

Teaching computers empathy

Teaching computers empathy

Lana Novikova is on a mission to teach computers to “read” your emotions.
Why is this important? Her Toronto start-up, Heartbeat AI, is connecting the dots between artificial intelligence, cognitive sciences, consumer research and marketing to help organizations better know their customers, employees and patients – by understanding how they feel.

Is Your Company Emotionally Intelligent?

Emotional connections are at the heart of enduring or loyal customer relationships, as well as employee engagement. The difference between fleeting transactional customer interactions and experiences that leave a lasting impression – whether positive or negative – is whether the experience strikes an emotional chord with the customer. 

My Experience Working With Heartbeat

(by Deanna Danelon)
I began working for Heartbeat Ai as an intern through Cisco’s Women Entrepreneurs’ Circle program. This program connects University of Waterloo engineering students with female entrepreneurs to provide technical support and services so that women entrepreneurs can optimize their businesses. Though I haven’t been a part of the Heartbeat team for long, these past few months have provided some incredible experiences, and I have learned so much.

The greatest experience has been the opportunity to witness what incredible things women are capable of, thanks to founder Lana Novikova. Seeing Lana’s success and hard work as a women entrepreneur, despite working a tech field, has been incredibly inspiring. Working and studying in a male dominated field, such as engineering, it is easy to feel discouraged or inferior. Being a part of the heartbeat team has shown me that women are capable of absolutely anything. This team consists majorly of inspiring women who work hard to support each other, and reach success, defying all odds. This has provided me new found confidence that being a woman is not a setback, all we have to do is support one another.

One of the most important things I’ve learned since working here has been how important empathy is. Having a technology that can understand emotions is a complete game changer. We are not human without our emotions, and having a technology that we can work with to help better understand emotions is a huge step towards greater human potential.

I am so lucky to have become a part of Lana’s team and to have been able to gain the experiences I did. I work with incredible, inspiring, and hardworking people, who are working to help make the world more empathetic.

The Future of Emotionally Intelligent AI

Society will get the AI it deserves.”

- Joscha Bach, What to Think About Machines That Think

Today, AI-powered machines can defeat the most skilled players in chess, a game of Go, Jeopardy, and poker. They can outperform best doctors in diagnosing most complex health conditions. Yet when it comes to understanding a wide range of human emotions, AI is not very intuitive. In his 2016 Google talk, Professor David Gelernter said: “ Understanding emotion as an encoding and simulating function is one of the most important unsolved problems - in fact, ignored problems and missed opportunities in AI today.”

Why do we need to teach thinking machines to understand human emotions? While applications for emotionally intelligent AI are too many to list here, let me mention three areas that we, at Heartbeat AI, are most fascinated about.

Deeper understanding and better prediction of irrational human behavior

We don’t need to try hard to convince anyone today that human beings are emotional first and rational second. Decisions that we make every minute - from what flavor of Starbucks to buy to who to vote for - are made deep inside our emotional minds influenced by many conscious reasons and sub-conscious “primes.”

Last Summer, we decided to run an experiment and see if we could build a predictive algorithm for national elections using unstructured text data from surveys. Traditional election polling asks questions like “If elections were held today, who would you vote for?” or “Who do you think will become the next Prime Minister of Australia?”. We picked 3 traditional questions and added two open-ended questions asking how people feel about each candidate winning the elections. The survey ran on Google Survey Platform for 6 weeks prior to the June 2nd Australian elections and November 8th US elections. Then we used a predictive model to call the results. In both cases, emotional measurements helped improve the level of predictability, and therefore made correct predictions for Australia (predicted for Bill Shorten vs. Malcolm Turnbull: 44% vs 56%, actual: 48% vs. 52%) and US (Donald Trump vs. Hillary Clinton):

  • Florida: predicted 52% versus 48% actual: 51% vs. 49%;

  • Iowa: predicted 58% vs. 42%, actual 55% vs. 45%;

  • Ohio: predicted - 63% vs. 37%, actual - 54% vs. 46%;

  • Pennsylvania: predicted - 54% vs. 46%, actual - 51% vs. 49%

What was even more interesting is that the models showed what particular emotions drove voters’ behavior, which explained the deep “why” behind people’s decisions.

Emotion text analytics is a new, possibly groundbreaking, area of research and analytics. What if we could rely on text data to be the “secret sauce” for accurate prediction of future events that are based on human decisions, such as elections, consumer behavior, public opinion and social movements.

Apps that will help people deal with stress, change, and suffering

Eight years ago, I took a break from my career in market research and went to study psychotherapy. I had a small private practice for a couple of years, helping people deal with loss, trauma, distractive anger, and debilitative fear and anxiety. Inside my own family and friends, I witnessed hard struggles my loved ones fought with alcohol and drug addictions. At the end, I did not find the stamina to be a psychotherapist, but I never stopped wondering if good therapy could be scaled, free and available when and where people need it the most. Then two years ago, an idea of an algorithm that could recognize a wide range of emotions from language - written and spoken - came alive. That’s how Heartbeat was born.

This Spring, we are starting a pilot with a small residential drug and alcohol addiction rehab in Ontario. We will give people a chance to share how they feel in the most difficult moments of their lives, recognize and name their emotions, and track their progress. We hope to build predictive algorithms that could help prevent relapse and foster resilience.

This is just the first step in a long journey. With 6 billion people with access to mobile phones by 2020, apps can help us not only find a good restaurant or a Pokemon, but peace of mind too. In 2016, the Guardian stated that “AI could leave half of world unemployed”. Imagine the stress and anxiety this would trigger! AI-powered apps could emotionally support millions of people transition from losing a job to retraining for new careers, and building new lives.

Building the foundation for benevolent AI

“Artificial minds will be faster, more accurate, more alert, more aware and comprehensive than their human counterparts. AI will replace human decision makers, administrators, inventors, engineers, scientists, military strategists, designers, advertisers, and of course AI programmers,” writes Joscha Bach of MIT Media Labs. He continues: ” The motives of our artificial minds will (at least initially) be those of the organizations, corporations, groups and individuals that make use of their intelligence. If the business model of a company is not benevolent than AI has the potential to make that company truly dangerous. Likewise, if an organization aims at improving the human condition than AI might make that organization more efficient in realizing its benevolent potential.”

When something possesses that much power, its makers ought to carry a high level of responsibility. It’s absolutely paramount to ensure that our powerful machines have benevolent “souls”, including intentional kindness, deep understanding of human condition, and cognitive empathy. What if AI could help us solve the biggest Global challenges, help humanity create Global governance, social and corporate systems, and ultimately rebuild trust?

Heartbeat is building a platform for people to share emotions and feelings about “hot” topics, burning political issues, the future, and life in general. We want to use emotion text analytics to help people understand their own emotions, see emotions of others across the world, and with that, have a window into each other’s souls. I hope that a Global “empathy meter” will grow if instead of opinions and agendas, people share personal feelings and see how fear and anger drives us away from each other.

If we are honest with ourselves, we would admit that we get the partner and the government we deserve. At the end of the day, we will get the AI we deserve. 

(This article was published in RE-WORK - see here)

Lana's Talk @Dx3, Toronto: How To Create and Measure Emotional Connection To Brand Narrative

Speaker: Lana Novikova, Founder & CEO - Heartbeat AI

Description: “Human emotions and feeling are part of a complex system that is not yet completely understood.” Emotional connection to brands are a big part of the consumer experience today, and yet one of the most difficult aspects to understand, measure, and improve upon. Lana Novikova, founder & CEO of Heartbeat AI, is an expert in exactly that - understanding and measuring human emotions. In this session, Lana provided tips on how to better foster emotional connection in your marketing efforts.

Read more about Lana's talk  here.

Read more about Lana's talk here.

Where Are They Now? IIeX EU 2016 Competition Winner Heartbeat AI

At IIeX EU in 2016 the winner of the Insight Innovation Competition was Heartbeat AI. Now in honor of the next round of the IIeX EU Competition next week we touch base with Lana on the Heartbeat journey, where they are now and where they are going.

At IIeX EU in 2016 the winner of the Insight Innovation Competition was a unique emotionally-based text analytics solution led by our first female CEO winner, Lana Novikova of Heartbeat.ai. Now almost a year later, and in honor of the next round of the IIeX EU Competition next week, I decided it would be interesting to touch base with Lana on the Heartbeat journey, where they are now and where they are going. 

First, a bit about their SaaS platform in their own words:

True deep insights can’t be found among scores and scales. Numbers have a definite role in research, yet only words and actions can point to our true hearts’ desires. Like raw diamonds, words often need skilled work to transform into a genuine sparkle. Survey open-ended comments that contain deep insights into human emotions, needs and motivations, often get discarded. Manual data coding is time consuming and labour intensive, pricey and inconsistent from study to study. HEARTBEAT brought together market research, psychology, neuroscience, data analytics, and technology to enable researchers to find and polish these “insight diamonds” – quickly, affordably, and consistently.

You’ll notice the use of almost poetic language in their description, which is absolutely “on brand” for Lana and her team; they approached the development of their platform not as an exercise in technology advancement but as an effort to combine myriad disciplines to address a need to create scalable “understanding” tools. Market research was one example of use cases, but the needs of healthcare, public policy, education and other categories drove their thinking as much as anything else.

The result is a uniquely powerful, beautiful, and elegantly simple solution that can be used by novices and experienced pros alike, with an API based model that allows their tech to be plugged in to power or augment many different complementary platforms.

Read the full article here and watch the interview: