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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.

SG. What aspects of emotion does Heartbeat detect and measure? Do you adopt a particular emotion model?

LN. There are a few models and classifications of emotions developed by brilliant psychologists like Paul Ekman and Robert Plutchik, and even by Human-Machine Interaction Network on Emotion (HUMANE). I was inspired by a more intuitive model of W. G. Parrott (2001), originally described by Shaver in 1987. It has a tree structure and includes Primary, Secondary and Tertiary emotions. I also did a lot of reading about effective neuroscience, and tried to combine Parrott’s model with what I took from the work of J. LeDoux, R. Davidson, and J. Panksepp. Then my “practical life-long quant researcher” side took over and asked, “How is this segmentation going to be useful to a brand of chocolate, or a bank, or a political party?”

The art of analysing good quality text data lies in understanding (a) how to ask a good question, and (b) how to infer meaning from people’s answers.

We ended with a 2-level clustering of 99 complex emotions and feelings into nine primary emotions: Joy, Love, Trust, Anger, Fear, Disgust, Sadness, Surprise and Void (which is explicit lack of emotion like in “I don’t care”). We also added Body Sense (positive, negative and neutral) as a way to analyse words and phrases that don’t point to a particular emotion, yet are useful for understanding human perception overall, especially for marketing food and body care products.

Many words and phrases are coded into multiple emotion clusters. Would you agree that there is a large overlap between Disgust and Anger, or Love and Joy, or even Anger and Fear? For example, we put the word “terrific” into both Joy and Fear, and let context decide which emotion it is more likely to represent. This is the most challenging part of our journey — understanding how different context colors “terrific” into happy or unhappy expression. It’s the domain of Machine Learning that needs lots of training data. We are just scratching the service here, but this is also the most exciting part of my job.

SG. How does the tech work?

LN. Today, our tech is very simple yet very accurate. It’s called “bag of words”: 8,000+ words and phrases (including negation, metaphor, and other multigrams) professionally coded into categories and validated by skilled psychologists and psycholinguists. Our software consumes unstructured text from survey responses and social media, and produces a set of visualisations and charts in a simple elegant dashboard.

We do our best work when we analyse data from survey responses that focus on people’s feelings. Data like that produces over 30% affect words, and has the context controlled by researcher. Once the report is ready (which is almost instantly), we curate results by removing some words that do not apply to the report. This is how we deal with another industry challenge, ambiguity. Finally, to prove that we are very good at what we do – we show all words and phrases for each Primary emotion. You can click on any word and see exactly how it appeared in the original text – no “black boxes” here. Since our taxonomy reached 5,000, our match rate — the percentage of affect words that we recognise — is over 95%. Heartbeat is committed to accuracy, depth, and transparency.


A Heartbeat AI visualisation of national feelings.

SG. How is Heartbeat different or better than the competition?

LN. Heartbeat is different because it was created by a market researcher (myself) who spent hundreds of hours coding open ended survey data. My team built our award winning app for researchers and marketers who appreciate the depth of consumer insight. I love working with good quality data, and would choose quality over quantity any time.

Survey data is under-used and often abused. The art of analysing good quality text data lies in understanding (a) how to ask a good question, and (b) how to infer meaning from people’s answers. I believe Heartbeat is better for distilling emotions from open-ended survey questions than any other company on the market today including IBM/Alchemy and other powerful APIs. They can do a lot of advanced text analytics with huge amounts of data. We made it simple, transparent, and fun. Just check out our dashboards — clients love it! Another big differentiator is that we are 100% focused on emotions — not sentiment or basic emotions, but fine-grained feelings. Our reports can be useful for anyone – from a CEO and CMO to a brand manager to a CX analyst to an agency creative director.

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