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Emotion Analysis

  • 80% accuracy in emotion detection
  • Efficient real-time identification of emotions
  • Analysis of 7 basic emotions with less manual effort
  • Rates effective content of individual words
Emotion Analysis Examples
Emotion Analysis Content Sections

Everything About Emotion Analysis

Discover how AI-powered emotion analysis transforms customer experience management by detecting and understanding human emotions in text.

What is emotion analysis?

When it comes to analyzing emotions, there are two main schools of thought: emotion analysis and sentiment analysis. Sentiment analysis is concerned with measuring the overall tone of a text or document, usually in terms of positive, negative, or neutral sentiments.

Emotion analysis, on the other hand, focuses on identifying and measuring the intensity of discrete emotions, such as happiness, sadness, anger, and fear. It is one of the latest and most exciting applications of artificial intelligence. This process enables us to understand and interpret a text's emotional content. Emotion analysis from text can be used to gauge public opinion on a certain topic, or to understand how people feel about a product or company. Moreover, understanding negative feedback plays a significant role in customer retention rate: recent studies show that 91% of disgruntled customers will simply leave and never come back.

Why is emotion analysis important?

Emotion analysis software is a powerful tool to adjust today's changing consumer behavior. "Human" signals, such as emotional text or social media comments, are difficult to analyze and decode, yet these often demand immediate and concentrated customer engagement. Feelings are involved; misinterpretations may occur; cultural barriers may hinder deep insights. Now, more than ever, with increasing screen time and prevailing interactive technologies, people express their feelings on various occasions across platforms. These reactions piling up as loads of complex CX signals become noise.

Emotion analysis is particularly critical for understanding Gen Z consumers, who express themselves predominantly through social media platforms like TikTok, Instagram, Reddit, and YouTube. This generation communicates their experiences and emotions in ways that traditional CX measurement tools often miss entirely. By analyzing emotions across these platforms, businesses can uncover blind spots in their customer experience that would otherwise remain hidden. Learn more about Gen Z social media data and CX blind spots.

With new social media integrations, emotion analysis provides a quick understanding of consumer reactions across all the channels where Gen Z and other demographics are most active. It thoroughly examines positive and negative human emotions and sensitivity. This sensitivity highlights the differences in how people express their emotions. As a result, it is a vision and reveals people's complex feelings and motivations. In short, by examining the motives and desires of customers and users, emotion analysis reveals valuable accurate information.

What are emotion analysis examples?

Here are some real-world examples across industries and geographies where Alterna CX's AI-driven emotional customer experience software recognizes the mood of the people from text.

Emotion Analysis Examples
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May 28, 2022 4:25 pm Sadness

"Many employees, little attention. I am an older person, due to my previous situation, I suffered a slight fall and NO ONE, no employee helped me, well they did not even ask me, if it was okay"

G
Apr 16, 2022 11:34 am Anger

"No me pareció muy bueno y los dueños son unos groseros"

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May 28, 2022 4:25 pm Fear

"The 'explosion' of the Gas tanks at the end was just as awful"

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Mar 21, 2022 10:57 am Joy

"Original products and fast delivery. Would definitely recommend!"

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How does emotion analysis play a role in AI-driven CX management?

Emotion analysis using natural language processing (NLP) reduces churn, measures performances of frontend and customer service agents, unlocks upselling and cross selling opportunities. In today's traditional CX management approach, surveys often fail to reveal the root causes of customer emotional experience and sentiments. In fact, scores may vary based on many outside factors, including geographical bias and industry shocks. These variables make it difficult to perform reliable root-cause analysis using surveys alone. On the other hand, artificial intelligence and machine learning subset AI data so as not to merely register sentiments, but indeed to understand themes and trends —"emotions"—in customer feedback. Different data acquired this way are synthesized to make a relevant and comparable picture of discernible customer experience.

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How is emotion analysis useful?

Here are four key applications of Alterna CX's emotion analysis tool for improving customer loyalty and CX quality:

1. Optimize Customer Treatment

Emotion analysis enables businesses to understand and respond to customer emotions in real-time, allowing for personalized treatment at scale. By identifying customers experiencing negative emotions like frustration, anger, or disappointment, customer service teams can prioritize urgent cases and tailor their approach to each situation. This emotional intelligence helps transform potentially negative experiences into positive ones, significantly improving customer satisfaction and retention. Companies can also track emotional patterns across different touchpoints to identify where customers need the most support and optimize resource allocation accordingly. Learn more about capturing the voice of customer through emotion analysis.

2. Detection of Personas with the Strongest Opinions

Emotion analysis can tell CX professionals which customers feel disgusted or angry. For example, this technology can promptly deduce the accurate cause-and-effect understanding of people who are disgusted by niche pricing. Likewise, emotion analysis can help marketers recognize which customers are feeling enough pride or trust to make them potential brand ambassadors.

3. Track Customer Feelings vs. Time

Natural language processing techniques like sentiment analysis and emotion analysis automatically classify trends and achieve high accuracy for identifying the most likely causes. For example, you could see a rise in consumer feelings of "sadness and fear," then trace those data back to their root cause with a subfield of natural language processing, emotion analysis in real-time.

4. Better Marketing Offers

By understanding the emotional context behind customer feedback and behavior, marketers can craft highly targeted campaigns that resonate on an emotional level. Emotion analysis reveals which product features or messaging trigger joy, excitement, or trust, enabling teams to double down on what works. It also helps identify the emotional pain points that drive purchasing decisions, allowing for more compelling value propositions. Discover how Alterna CX's product helps create data-driven marketing strategies based on emotional insights. This leads to higher conversion rates, better ROI on marketing spend, and more meaningful customer relationships built on emotional understanding.

Emotion Analysis Useful Cases
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How do we help?

With our emotion analysis technology, CX professionals can accurately extract what emotions customers or users experience when using products or services every day. Did the specific transaction create "delight" or was it a "disappointing" experience? Is the process "confusing" or does it "work like a charm"?

Alterna CX analyzes emotions across hundreds of public sources including review sites, social media platforms, as well as internal sources. This comprehensive approach means Alterna can detect emotions in key employee and customer journeys, providing a complete picture of emotional experiences across all touchpoints.

Traditionally, CX professionals use sentiment analysis to understand whether an experience was positive or negative. Applying this approach, however, it is not possible to differentiate between a moderately happy customer comment and that of a delighted customer or, conversely, a slightly disappointing user comment versus a very angry user comment. The tool bridges this gap by enabling a granular look at customer emotions for better experience analysis and design.

Alterna CX also combines metric-based experience data such as Customer Effort Score or Net Promoter Score with emotion models. This machine learning algorithm enables CX professionals to analyze the effects of key human emotions on decisive business metrics such as NPS, retention, and revenue.

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How does Alterna CX's emotion analysis work?

Our emotion model scans through text-based data created by customers in surveys, complaints, social or service. Inspired by Dr. Robert Plutchik's emotional wheel study of the seven most frequently felt human emotions (joy, trust, fear, surprise, sadness, anger, and disgust), our engineering team developed this AI-based advanced text analytics technology. Our emotion analysis from text is ready to use but also flexible enough so that "custom emotions" may be trained as required for specific company needs.

By evaluating emotions in real time, CX professionals can identify the problems, opportunities and issues that contribute the most to CX quality and customer loyalty, prioritize improvement decisions accordingly, and initiate action in the form of an immediate fix, a re-design, or another type of improvement.

How Emotion Analysis Works

FAQ

Can AI understand emotions?
This tool gives us the ability to understand and interpret the emotional content of the text. Emotion analysis technology can be used to gauge public opinion on a certain topic, or to understand how people feel about a product or company. Alterna CX's AI-based software can detect emotions such as joy, anger, disgust, happiness.
How to analyze emotions?
Our emotion model scans through text-based data created by customers in surveys, complaints, social or service. Inspired by Dr. Robert Plutchik's emotional wheel study of the seven most frequently felt human emotions (joy, trust, fear, surprise, sadness, anger, and disgust), our engineering team developed this AI-based advanced text analytics technology. Our emotion analysis from text is flexible enough so that "custom emotions" may be trained as required for specific company needs. By evaluating emotions in real time, CX professionals can identify the problems, opportunities and issues that contribute the most to CX quality and customer loyalty, prioritize improvement decisions accordingly, and initiate action in the form of an immediate fix, a re-design, or another type of improvement.
What emotions can Alterna CX's emotion analysis detect?
Alterna CX's emotion analysis is based on Dr. Robert Plutchik's emotional wheel and can detect seven primary human emotions: joy, trust, fear, surprise, sadness, anger, and disgust. Additionally, our system is flexible enough to train custom emotions based on specific business needs. This allows organizations to identify nuanced emotional states that are particularly relevant to their industry or customer base, providing deeper insights into customer experiences beyond standard emotion categories.
How accurate is emotion analysis compared to traditional sentiment analysis?
Alterna CX's emotion analysis achieves 80% accuracy in emotion detection and provides significantly more nuanced insights than traditional sentiment analysis. While sentiment analysis only categorizes feedback as positive, negative, or neutral, emotion analysis identifies specific emotions and their intensity levels. This means you can distinguish between a customer who is mildly disappointed versus one who is extremely angry, or between someone who is satisfied versus someone who is truly delighted. This granular understanding enables more precise and effective responses to customer feedback, leading to better business outcomes.
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