What is emotion analysis, and how can it be used?
Back when I was still a linguistics student, I did a research about the effectiveness of political propaganda by analyzing the nonverbal cues of political leaders, the crowd's noise level, and the sentiment analysis of social media mentions to evaluate the impact of their campaigns. And I must say, for me, the results were unexpected. I realized that the emotions evoked in people are more important than the message conveyed because, no matter the context of the promise, people tend to gravitate towards leaders who show affection and confidence.
Emotion is a well-studied subject in linguistics and psychology, and emotion analysis is a highly used method in analytical marketing, but what is emotion analysis?
What is emotion analysis?
Emotion analysis refers to the process of detecting, identifying, and measuring the emotional state conveyed in textual, visual, and auditory content, such as anger, disgust, fear, happiness, sadness, and surprise.
Big companies use emotional analysis in their marketing campaigns to improve customer experience and shape customer personas. This is done by using platforms that analyze text for emotional keywords (Natural Language Processing), examine facial expressions in videos or images (computer vision), and interpret vocal tones in audio(SER).
For example Google Cloud Video Intelligence API Can analyze video content for various features, including facial expressions to detect emotions and Beyond Verbal is specialized in analyzing human voice for emotions and behavioral insights.
Marketers and businesses use these tools to analyze how people react to their ads, videos, and other content.
The hotel chain Hilton recently used emotion analysis by placing secret cameras that detect face muscles and interactions of the individuals at a trade show. This was used to change the event planning from open bar to puppies and ice cream event.
In 2025, as user habits started to change and tech increase its importance, a developer submitted a project for emotion analysis as part of a competition on the Google AI for Developers platform.
Jeong Gaon built a tool designed to analyze text and identify the underlying emotions expressed using Google Gemini API. It's stated that, "The API processes the user's emotional input and uses advanced algorithms to determine the specific emotion. This enables the app to deliver personalized content.".
Developments in Emotion Analysis
- Affectiva: On March 21, 2018, Affectiva launched Affectiva Automotive AI, the first multi-modal in-cabin sensing solution to understand what is happening with people in a vehicle. (Source: Affectiva - Wikipedia)
- Google Cloud Vision API: On April 1, 2016, Google announced the newest feature of the Cloud Vision API: style detection which included the ability to recognize human emotions. (Implied launch from the YouTube video title and content: Introducing Style Detection for Cloud Vision API - YouTube)
- Amazon Rekognition: On August 13, 2019, AWS announced that Amazon had updated the range of detectable emotions for Rekognition's face analysis to include “fear,” adding to a list of seven other emotional states. (Source: Amazon Says The Face Recognition Tech It Sells to Cops Can Now Detect 'Fear' - VICE).
- Smart Eye (acquired Affectiva): In January 2024, Smart Eye announced an "innovative emotion generative AI capability" at CES 2024. This technology merges advanced automotive sensing with large language models to allow in-car assistants to recognize emotions for dynamic human-vehicle interactions. (Smart Eye Announces Innovative Emotion Generative AI Capability and Key Partnerships at CES 2024)
- Microsoft Azure AI Vision: While not a specific launch of new emotion analysis capabilities, Azure AI Vision had updates in February 2025 related to face liveness detection, indicating continued development in facial AI. They also mention emotion recognition as a feature of the Google Cloud Vision API in their documentation (What's new in Azure AI Vision? - Learn Microsoft).
Emotion vs Sentiment Analysis: Are they the same?
Sentiment analysis and emotion analysis are related fields within Natural Language Processing (NLP) that aim to understand the underlying feelings expressed in text and other mediums of context but they focus on different aspects.
Sentiment analysis primarily concerned with identifying the overall polarity of a text or a specific piece of information.
Goes beyond simple polarity and aims to identify the specific emotions expressed in the context.
To demonstrate the difference let's look at a comment from Tabular's Product Hunt profile:
I take this comment and send it through Google's Natural Language Processing (NLP) service to analyze the sentiment.
Google NLP analyzes the text and provides me with sentiment scores for the entire comment and each individual sentence, indicating that the comment is very positive.
Below video uses Computer Vision and Facial Recognition technologies for analyzing facial movements and expressions (e.g., smiles, raised eyebrows, narrowed eyes) to predict emotions such as calmness, surprise, disgust, happiness, fear, sadness, anger, and thinking states.
The primary difference between Sentiment Analysis and Emotion Analysis, lies in the depth of interpretation and the type of emotional insight provided.
Sentiment analysis focuses on evaluating the overall emotional tone or polarity of content, categorizing it broadly as positive, neutral, or negative. It doesn't explore the subtleties of emotions, instead offering a generalized summary that’s useful for quickly assessing user feedback, public opinions, or brand perception.
In contrast, emotion analysis goes beyond, identifying precise emotional states and reveals which exact emotions are present.
Granularity, in this context, refers to the level of detail each method captures: sentiment analysis is low-granularity, giving an overarching sense of positivity or negativity without detailed emotional breakdown, while emotion analysis has high granularity, as it distinctly recognizes and differentiates between multiple emotional states simultaneously.
| Sentiment Analysis | Emotion Analysis | |
|---|---|---|
| Primary Goal | Determine overall polarity (positive, negative, neutral) | Identify specific emotions (joy, sadness, anger, etc.) |
| Output | Polarity label or score | Emotion labels with potential confidence scores |
| Complexity | Generally less complex | Generally more complex |
| Focus | Broad overall feeling | Specific types of feelings |
The difference in uses of sentiment analysis and emotion analysis
Use cases of sentiment analysis:
- Analyzing customer reviews to understand overall satisfaction with products or services.
- Monitoring social media to track public opinion about brands, events, or political figures.
- Managing online reputation by identifying and addressing negative feedback.
- Filtering and prioritizing customer support tickets based on the urgency indicated by negative sentiment.
- Analyzing news articles or financial reports to understand market sentiment.
Use cases of emotion analysis:
- Improving customer service interactions by understanding the specific emotions of customers (e.g., frustration, anger).
- Analyzing the emotional impact of marketing content or advertising campaigns.
- Understanding audience reactions to movies, music, or other forms of entertainment to be referenced in marketing campaigns.
- Developing more empathetic and responsive AI assistants or chatbots.
Data Sources and Tools for Emotion Analysis
- Brandwatch: Brandwatch is a social media software company providing market research, consumer research, social media analytics, monitoring, and management. (Source: Brandwatch - Wikipedia)
- NetBase Quid: NetBase Quid is a social intelligence platform used for brand analysis, market research, and understanding consumer conversations. (Source: NetBase Quid website)
- Zoho CRM: Zoho CRM is a customer relationship management software that includes features for analyzing customer sentiment. (Source: Zoho CRM website)
- Thematic: Thematic is a software that collects feedback channels and builds customized deep learning models to extract actionable insights. (Source: Chattermill Wikipedia mentioning Thematic)
- IBM Watson Natural Language Understanding: IBM Watson Natural Language Understanding is a service that uses cognitive computing to analyze text and extract meaning, including sentiment and emotions. (Source: IBM Cloud website)
- Google Cloud Natural Language: Google Cloud Natural Language provides an API for sentiment analysis and understanding the meaning and structure of text. (Source: Google Cloud website)
- Azure Text Analytics: Azure Text Analytics is a cloud-based service that provides natural language processing features including sentiment analysis. (Source: Microsoft Azure Wikipedia)
- Chattermill: Chattermill is a customer feedback analytics platform that uses AI to understand customer conversations and identify underlying emotions. (Source: Chattermill website)
- Kapiche: Kapiche is an AI-powered sentiment analysis tool designed for customer insights teams to quickly and accurately analyze unstructured customer data. (Source: Kapiche website)
- Brand24: Brand24 is an internet and social media monitoring tool. (Source: Brand24 - Wikipedia (Polish version, translated))
- Qualaroo: Qualaroo is a platform used to gather real-time customer feedback and includes features for analyzing the sentiment of responses. (Source: Qualaroo website)
- OpenText: OpenText is a company that provides various enterprise software solutions, including content management and text analytics that can analyze sentiment. (Source: OpenText website)
- Comments Analytics: Comments Analytics is a tool focused on analyzing online comments and identifying sentiment. (Source: Analytics - Wikipedia mentioning the application of analytics to areas rich with recorded information)
- Pangeanic: Pangeanic is a company offering multilingual language processing services, including sentiment analysis in various languages. (Source: Pangeanic website)
- InMoment: InMoment (which acquired Lexalytics) provides a platform for customer experience improvement, including text analytics with sentiment and emotion detection. (Source: InMoment website)
- Medallia: Medallia is a customer experience management platform that uses video analysis to detect emotions through facial expressions. (Source: Medallia website)
- Viso Suite: Viso Suite is an AI vision platform that offers real-time emotion recognition from facial expressions in video streams. (Source: Viso Suite website)
- Moodme AI: Moodme AI offers AI-powered emotion recognition software that analyzes facial expressions in images and videos. (Source: Moodme AI website)
- MorphCast AI: MorphCast AI provides technology that analyzes facial expressions in pictures and videos for emotion detection. (Source: MorphCast website)
- iMotions: iMotions is a software platform used for human behavior research, including facial expression analysis for emotion recognition. (Source: iMotions website)
- FaceReader (by Noldus Information Technology): FaceReader is software for the automatic analysis of facial expressions. (Source: Noldus Information Technology website)
- Affectiva: Affectiva (now part of Smart Eye) was an AI software development company that claimed its AI understood human emotions by analyzing facial and vocal expressions. (Source: Affectiva - Wikipedia)
- SentiSum: SentiSum is a customer experience analytics platform that uses AI to collect, analyze, and report on customer conversations, including identifying emotions. (Source: SentiSum website)
- InMoment: InMoment provides a comprehensive platform for improving customer experience, which includes deep emotion analysis of customer feedback across various channels. (Source: InMoment website)
Wrapping Up
It's interesting to see how far the field of studying human emotions has come since I first started studied emotional analysis.
What began as an interest in body language and how people feel in groups has grown into a complex field with high-tech tools from companies like Google, Amazon, Microsoft, and Affectiva.
Emotion analysis is no longer just a special field of study; it has become an important tool for businesses that want to connect with their customers on a deeper level.
By looking into the subtleties of how people feel through text, voice, and facial reactions, businesses can learn a lot that helps them improve their services and relationships.
As technology keeps getting better and AI gets smarter, being able to correctly and morally analyze feelings will become even more important in how we use technology and talk to each other online.