We use cookies to optimize experience and functionality.  Learn more about cookie policy

Emotion Analysis: Definition, models and use-cases

Emotion analysis is the process of detecting consumer emotions with the help of AI and machine learning technologies. Learn more on how it can help you make informed business decisions.
10 Min Read    |   April 13, 2024

Table Of Contents

    A marketer worth his salt knows that emotions have the power to influence consumer behavior and actions.

    Customers naturally gravitate towards the brands, products, and services that make them happy, strictly avoiding those that make them sad.

    It’s the same with marketing ads and campaigns.

    As per psychology, emotions are strong mental reactions that occur in every individual. These reactions are in response to certain changes in the environment and are experienced as feelings.

    So if you know what makes people tick, you can easily control their emotional responses.

    Businesses that understand consumer emotions can improve the customer experience and forge deeper connections.

    Emotion analysis further simplifies this process, helping you detect underlying emotions that are not evidently visible and build better marketing strategies.

    What is emotion analysis?

    Emotion analysis is the process of identifying and extracting human emotions from vast amounts of textual, visual, or auditory data.

    It’s kind of like sentiment analysis, in the sense that they both use similar data sources. However, emotion analysis gives you a much more holistic view of your customers’ feelings and emotions.

    But more on that later.

    Nowadays, tech giants like IBM are integrating artificial intelligence and machine learning to measure customer sentiments and emotions.

    Take for example, IBM’s Watson. It uses deep learning models to infer emotions like anger, disgust, fear, joy, or sadness from unstructured textual data.

    Types of emotion AI

    Emotion AI is a branch of artificial intelligence that helps machines understand, replicate, and respond to human emotions.

    Known as affective computing, it combines computer science and psychology to facilitate empathetic interactions between humans and computers.

    You can leverage it to analyze your customers’ tone of voice and expressions. This will help you figure out the emotions expressed and offer responses in real-time.

    While there are different types of emotion AI models available today, these are the main ones.

    Types of emotion AI

    1. Text analysis

    In textual analysis, a piece of written or spoken language is processed to comprehend the feelings expressed in the text.

    Initially, a large volume of data is analyzed and classified into different sentiments using Natural Language Processing (NLP) technologies and sentiment analysis algorithms.

    Classification can be at the sentence-level, paragraph level, or the document level.

    Textual data is then broken down into fine-grained emotions like anger, happiness, or sadness to determine the overall emotional context.

    You can perform text based analysis on customer feedback, surveys, reviews, social media posts, and customer support chats.

    2. Visual analysis

    Machines can analyze images, videos, and facial expressions to determine the emotions expressed by individuals.

    A facial recognition software can detect expressions that are too fast for the human eye, like subtle muscle twitches and brow movements, to identify a variety of emotions.

    Even so, it’s not always accurate.

    Static images are easier to classify, but dynamic visuals like real-time videos are more complex, since people can fake expressions.

    3. Speech/audio analysis

    Unlike text based emotion AI, which is fairly simple, speech analysis requires algorithms that can process audio datasets.

    The algorithms identify emotions on the basis of voice features like tone of voice, pitch, tempo, speech patterns, accents, and other cues.

    This type of emotion analysis technology is often used in customer services and call centers to assess caller sentiments and improve service quality.

    Three key elements of emotion

    It’s interesting to think that no one felt emotions before the 1800s. Instead, people had fits of ‘passion’ or ‘affection.’

    It makes sense since emotions came around only in the 1830s. Dating back to the sixteenth century, the term was derived from the French word 'émouvoir,' which means 'to stir up' or 'displace.’

    The James-Lange theory suggests that emotions are made up of three elements, namely:

    1. Subjective experiences
    2. Physiological responses
    3. Behavioral responses

    All three elements are interconnected. Let’s take a look at each of these in more detail.

    Three key elements of emotion

    #1 Subjective experiences

    You don’t feel emotions without reason. In fact, you need external factors or stimuli to spurn you along. While basic emotions like anger and happiness are experienced by everyone, the way in which they express it can vary.

    Take Japan, for instance. The country has a culture that places a strong emphasis on “wa,” which basically translates to social harmony.

    Hence, the Japanese tend to suppress negative emotions in social settings. Positive emotions are often expressed through subtle gestures and behavior.

    This is not just the case with distinct nationalities and cultures.

    Even on the individualistic level, emotions are subjective. Depending on the person and the scenario, the quality and intensity of the emotion felt will be different.

    Customers watching a funny advertisement can either feel mild amusement or solid excitement based on the impact it has on them.

    The emotional response can be triggered by the message or the visuals.

    #2 Physiological responses

    Physiological responses are instinctive reactions to external or internal changes in the environment. They have helped us scale the evolutionary ladder.

    That being said, emotions can cause strong physiological changes in your body.

    Here’s how.

    Imagine that you are on a jungle safari and suddenly get attacked by wild animals. What are the emotions you will feel?

    An initial sense of surprise that is quickly replaced by fear, right?

    Your heart will start racing, your hands will start to sweat, and your muscles will tense up. This fight-or-flight response is due to the release of adrenaline in your body.

    The involuntary physical changes are the result of the autonomic nervous system’s (ANS) reaction to the fear you are experiencing.

    A funny ad might elicit a similar, albeit a positive physiological reaction in viewers. People may start smiling or laughing with the release of "happy hormones" like endorphins.

    #3 Behavioral responses

    Different behavioral expressions are what makes it possible for us to tell what another person is feeling.

    If someone is smiling, they are probably happy. If they show signs of aggression, like a furrowed brow or clenched fists, they are likely to be angry.

    But not all expressions convey the same meaning.

    Remember that our society, culture, and personality plays a major role in how we express ourselves.

    It’s easier to be open about emotions in Western countries like America, which prioritize individualism and self-expression, as compared to Eastern countries like Japan.

    So while Americans might laugh out loud at a humorous ad, the Japanese might simply share it with others online.

    Emotion analysis vs sentiment analysis: How are they different?

    Although used interchangeably, emotion analysis and sentiment analysis are two different concepts.

    Sentiment analysis, also known as opinion mining, primarily focuses on polarities. It determines whether a user has positive, negative, or neutral sentiments with regards to your product, feature, or brand.

    It works well for companies who want a general overview of the success of their marketing campaigns and product launches.

    However, sentiment analysis is somewhat subjective, since what might be considered to be a positive sentiment in one context might be negative in another.

    Emotion analysis is a more granular approach to sentiment analysis. It goes beyond positive and negative polarities and looks at the finer points of a buyer’s emotions.

    Here’s a simple representation:

    • Polarity > Emotion Category > Emotions
    • Negative > Anxiety > Worry, Distress

    While sentiment analysis is sometimes useful, it is insufficient in scenarios that require a better understanding of customer emotions.

    Types of emotion analysis models

    Psychologists have desperately tried to understand the emotions that make up the human population and come up with many theories over the years.

    And why not? Emotions play a crucial role in our lives.

    Currently, emotion analytics relies heavily on textual analysis for processing customer emotions. This approach involves NLP technologies that use different emotion models.

    Now there are two main models for classifying emotions:

    • The categorical model
    • The dimensional model

    Both models help detect emotions and provide insights into how emotions are perceived by the human mind.

    I. Categorical model

    The categorical model of emotion analysis places a person’s emotions into six basic categories, like anger, fear, disgust, joy, sadness, and surprise.

    Specific words are linked to relevant emotion tags and used to detect both related and unrelated emotions.

    You can also go beyond the basics and include four to eight categories.

    The categorical model sounds simple and effective but it does come with its own problems.

    1. It does not include all emotions, as most of them are clubbed under a single category.
    2. People may choose from predefined classes rather than come up with a new emotion themselves.
    3. Cultural and linguistic differences might prompt people to label the same emotions differently.

    II. Dimensional model

    Emotions under the dimensional model are presented on the basis of three parameters: valence, arousal, and power.

    • Valence stands for polarity, which describes the positivity or negativity of an emotion
    • Arousal displays the intensity of an emotion
    • Power signifies the degree of control one has over an emotion

    Emotion related terms are usually placed in a circumplex shape, which can be either two dimensional (valence and arousal) or three dimensional (valence, arousal, and power).

    The following sections will give you an example of both the categorical model and the dimensional model.

    Paul Ekman's theory of basic emotions

    Paul Ekman is a renowned psychologist who theorized that human beings experience six basic emotions: Happiness, sadness, anger, fear, disgust, and surprise.

    His theory is primarily employed in categorical emotion analysis.

    According to Ekman, some emotions are universal and expressed through distinct facial expressions, regardless of cultural, linguistic, or societal influences.

    Let’s examine their definitions as well as the facial expressions that accompany each of them:

    Examples of the Ekman-Friesen Pictures of Facial Affect used in the computerized task.

    Happiness: An emotion marked by smiles and laughter. People who are happy often have raised cheeks and crow's feet at the corner of their eyes.

    Sadness: Includes grief, sorrow, distress or disappointment. It is generally characterized by a downturned mouth, drooping eyelids, and/or crying.

    Anger: Narrow eyes, furrowed brows, and a tense jaw points towards a person who is irritated or furious.

    Fear: Individuals who are in a state of alarm or panic have wide eyes, raised eyebrows, and a tense mouth.

    Disgust: You can be disgusted by something or someone. This aversion can manifest in the form of a wrinkled nose and upper lip.

    Surprise: Refers to amazement or astonishment. Surprise, both good and bad, is indicated by wide eyes, raised eyebrows, and an open mouth.

    Ekman further expanded his list to include emotions like contempt, excitement, shame, pride, satisfaction, and amusement.

    The 2D valence-arousal model of emotion

    We know that emotion analysis models have been largely derived from works of psychology and used to interpret customer behavior.

    The 2D valence-arousal model of emotion, or the circumplex model, is one such framework that categorizes human emotions into a two dimensional space.

    It represents emotions on the basis of two dimensions:

    • Valence (positive to negative)
    • Arousal (high to low)

    Valence is the emotional quality or pleasantness of an emotion. It ranges from positive to negative.

    Emotions on the positive side of the spectrum tend to be associated with feelings of happiness, joy, and contentment, while those on the negative end represent anger, anxiety, and fear.

    Neutral feelings, or those that are neither positive nor negative, include things like boredom and listlessness.

    Arousal displays the intensity or magnitude of an emotion. It can be high, low, or neutral.

    Low-arousal emotions are typically subdued (like relaxation and boredom), while high-arousal emotions are stimulating (such as anger, fear, and excitement).

    Emotions can be represented at any level of valence and arousal, or at a level neutral to one or both of these dimensions.

    The 2D valence-arousal model of emotion

    Besides marketing, the valence-arousal model is often used in human-computer interaction (HCI) to design and develop better user experiences.

    What is the use of emotion analytics?

    Doing a business is all about acquiring customers and making profits. However, it is not very easy to get prospects that trust you these days.

    Emotion analytics is the crucial component that will empower you to acquire users even in this ever evolving landscape of consumer behavior.

    By combining emotion analysis and business intelligence, you can test new product designs, reshape marketing, and improve customer service.

    Uses of emotion analytics

    Learn more about how you can do it in the following sections.

    #1 Test new product designs and ideas

    Emotion analysis uses advanced AI technologies that can analyze large amounts of research data to identify industry trends.

    You can gain insights into how users emotionally connect with a product, helping you test its market potential and avoid wasting financial resources.

    Additionally, you can:

    • Collect emotional feedback from users interacting with new designs
    • Identify design elements that cause frustration or confusion
    • Reinforce positive emotions and resolve negative ones
    • Speed up the iterative design process (prototyping, testing, refining)

    Based on the emotional state of the customer, your product can then be adjusted to offer a more personalized user experience.

    #2 Optimize ads and marketing strategies

    Harvard Business Review reports, “Within a year of launching products and messaging to maximize emotional connection, a leading household cleaner turned market share losses into double-digit growth.”

    It shows that having an emotional rapport with your buyers matters.

    The best way to achieve a connection is through emotion analytics. It provides marketers with the insights they need to create campaigns that emotionally resonate with their audience.

    In the dynamic realm of digital media, you can use emotion analytics tools to:

    • Gain insights into customer needs, pain points, and triggers
    • Create engaging content based on emotion analysis metrics
    • Explore brand-related conversations on social media
    • Improve customer relationship management
    • Optimize advertisements by analyzing viewer emotions
    • Create targeted videos content based on expressions

    You can integrate data analytics with emotion analytics to make data-driven decisions. It will enable you to build a solid marketing strategy that increases ad engagement and performance.

    #3 Offer a great customer service

    Emotion analysis has gained significant importance in the customer service industry.

    You can now use audio analysis to grasp the emotions behind a customer’s voice, which allows you to better understand the needs of your buyers.

    Emotions recognition software can further help you gain competitive insights. Here are some of the ways in which you can achieve this:

    1. Analyze customer sentiments and feedback

    Your customer service staff can use speech analysis tools to monitor the sentiment, tone, and feedback provided by your buyers.

    2. Improve customer experience

    Certain tools can guide your team members by suggesting tone adjustments, speed modifications, and empathy display, which can improve interactions.

    3. Offer personalized recommendations

    By collecting user data and analyzing customer emotions, brands can not only solve queries and complaints but also provide customized product recommendations.

    4. Develop better chatbots

    Users expect personalization at each step of the customer journey.

    It’s the same with chatbots. They want AI assistants that can analyze their moods and respond to their questions accordingly.

    Emotion analysis enables chatbots to deliver genuine responses, adapt to conversations, and express empathy, effectively meeting user expectations.

    Wrapping up

    Major tech giants like Apple, Microsoft, and IBM are investing in and providing emotion analytics tools that can aid your business decisions.

    Whether those decisions involve marketing, sales, or service, companies in ecommerce and technology are already using emotion analysis to measure customer satisfaction.

    Despite concerns around data collection and privacy, you can leverage emotion analysis to enhance your business performance.

    Frequently Asked Questions (FAQs)

    What is emotion analytics?

    Emotion analysis is the process of identifying human emotions from large amounts of textual, visual, and audio data. Emotion analytics software is used to extract this data and provide insights into individual moods, emotions, and attitudes.

    What is the difference between emotion analysis and sentiment analysis?

    Emotion analysis looks at distinct human emotions that go beyond positives or negatives, offering a granular understanding of consumer sentiments. Sentiment analysis, on the other hand, focuses on polarities — determining whether user sentiments are positive, negative, or neutral — providing a general and subjective overview of your brand reputation and product popularity.

    What is the emotion analysis method?

    Emotion analysis studies human emotions using AI and machine learning technology. Generally speaking, there are three types of emotion AI models that you can use to conduct emotion analysis: textual analysis, visual analysis, and audio analysis.

    • Textual analysis analyzes written or spoken language to understand customer emotions.
    • Visual analysis uses photographs, videos, and facial expressions to gauge the emotions expressed by people.
    • Audio analysis processes audio datasets (speech, voice, pitch, tempo, accents) to determine individual emotions.
    What are the three concepts of emotion?

    According to the James-Lange theory, emotions are made up of three main elements: subjective experiences, physiological responses, and behavioral responses. All three elements are interconnected to each other and shape the way humans feel things.

    Like what you're reading? Sign up for our email newsletter!
    Learn about personas, competitor analysis, and audience research

    Related articles

    What is Customer Sentiment Analysis?

    What is Customer Sentiment Analysis?

    Customer sentiment analysis is the process of detecting and understanding the opinions, emotions, and attitudes of consumers towards a certain product, service, or company. Discover its use cases and applications.
    10 min read
    Consumer personality traits (Big Five)

    Consumer personality traits (Big Five)

    Learn about Big Five personality traits and how they can be used in marketing to understand consumer behavior and decision-making processes and to optimize marketing messages.
    11 min read
    Psychographic segmentation: Examples and variables

    Psychographic segmentation

    Psychographic segmentation groups customers based on their psychological traits and attributes. Explore variables and take a look at important examples to better understand your target market.
    8 min read
    View all blog articles ->

    Subscribe for blog updates

    Our products

    Customer Persona

    Build data-driven personas
    from your customer/CRM data

    Social Persona

    Generate personas for your
    social media audience

    Competitor Persona

    Develop personas for competitors
    using Delve AI's intelligence data