During a survey conducted in France, Germany, the United Kingdom, and the United States in September 2021, more than half – ** percent – of responding adults said a business being located in their area made them feel emotionally connected to it. A product or service that exceeded expectations ranked second, named by ** percent of the interviewees. Approximately ** percent of respondents said nothing would make them emotionally connected to a company. According to that same sample, the leading characteristic of a great customer experience (CX) was a friendly and helpful approach.

The Emotional Marketing Service market is a rapidly evolving industry focused on connecting brands with consumers on a deeper emotional level, leveraging consumers' feelings to drive engagement, loyalty, and sales. Emotional marketing services utilize techniques such as storytelling, personalized messaging, and comp

Between January and September 2023, the most prevailing emotion in Instagram posts relating to selected beauty brands (Anastasia Beverly Hills, Benefit Cosmetics, Fenty Beauty, Huda Beauty, Rare Beauty, and Too Faced) was joy - with **** percent of posts. Sadness was also measured as a social emotion in connection to Instagram beauty brands, with a significantly lower share of *** percent.

During a 2022 survey fielded in the United Kingdom, gauging their perception of advertising, ** percent of respondents stated that they found advertising that made them feel emotional on TV. Social media ranked second, named by ** percent of interviewed consumers.

Emotion Analytics Market Valuation – 2024-2031

Emotion Analytics Market is valued at USD 3.95 Billion in 2024 and is anticipated to reach USD 10.94 Billion by 2031, growing at a CAGR of 14.99% from 2024 to 2031.

Global Emotion Analytics Market Drivers

Increased Demand for Customer Insights: Businesses are increasingly seeking to understand customer emotions and preferences to improve their products, services, and marketing strategies.

Advancements in Artificial Intelligence and Machine Learning: The development of sophisticated algorithms and deep learning techniques has significantly enhanced the accuracy and efficiency of emotion recognition.

Growth of Social Media and Online Platforms: The vast amount of data generated on social media and other online platforms provides a rich source for emotion analysis.

Global Emotion Analytics Market Restraints

Data Privacy Concerns: The collection and analysis of personal data for emotion recognition raises concerns about privacy and ethical implications.

Accuracy and Reliability: While advancements in technology have improved the accuracy of emotion recognition, there are still limitations and challenges in ensuring reliable results across different contexts and individuals.

This statistic shows the results of a survey asking consumers in the United Kingdom "does an emotional connection to a retailer or brand make you more likely to purchase?". Of respondents, 27 percent would be a lot more likely and 54 percent a bit more likely to purchase from a retailer which they have an emotional connection to.

According to Cognitive Market Research, the global Emotion AI market size will be USD 2891.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 22.60% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1069.97 million in 2025 and will grow at a compound annual growth rate (CAGR) of 20.4% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 838.62 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 694.03 million in 2025 and will grow at a compound annual growth rate (CAGR) of 24.6% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 109.89 million in 2025 and will grow at a compound annual growth rate (CAGR) of 21.6% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 115.67 million in 2025 and will grow at a compound annual growth rate (CAGR) of 21.9% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 63.62 million in 2025 and will grow at a compound annual growth rate (CAGR) of 22.3% from 2025 to 2033.
Emotion recognition category is the fastest growing segment of the Emotion AI industry

Market Dynamics of Emotion AI Market

Key Drivers for Emotion AI Market

Increasing Adoption of AI-Driven Customer Service Solutions to Boost Market Growth

Emotion AI is gaining significant traction as businesses adopt AI-driven solutions to enhance customer service. As customer interactions become increasingly digital, companies are looking for ways to better understand and respond to consumer emotions in real-time. Emotion AI enables brands to personalize their communication by detecting emotional cues in voice, text, and facial expressions, allowing for more empathetic and effective customer engagement. This technology is being widely implemented in call centres, chatbots, and virtual assistants to improve customer satisfaction, resolve issues promptly, and boost brand loyalty. With growing consumer demand for more personalized experiences, businesses see Emotion AI as a powerful tool to build deeper, more meaningful connections with their customers.

Advancements in Deep Learning and Natural Language Processing (NLP) To Boost Market Growth

Recent advancements in deep learning and Natural Language Processing (NLP) have greatly enhanced the capabilities of Emotion AI, enabling more accurate and sophisticated emotion recognition. These technologies empower Emotion AI systems to better analyze and interpret emotional signals from voice, text, and facial expressions with higher precision. As deep learning models become more advanced, Emotion AI can better understand the nuances of human emotion, even in complex or ambiguous situations. NLP allows AI systems to process human language in a way that can detect sentiment, tone, and emotional states from textual data, opening up new applications in industries like healthcare, education, and entertainment.

Restraint Factor for the Emotion AI Market

Data Privacy Concerns Will Limit Market Growth

Emotion AI relies heavily on collecting and analyzing large amounts of personal data, including facial expressions, voice tones, and behavioural patterns. The collection of such sensitive information can raise privacy concerns among users. Data privacy regulations like the GDPR in Europe and CCPA in California create stringent rules about consent and data handling. As these regulations evolve, Emotion AI companies must ensure compliance to avoid legal challenges and penalties. Users' reluctance to share personal information or concerns about how their data is used could limit the widespread adoption of Emotion AI, as businesses and consumers are increasingly prioritizing privacy.

Impact of Trump Tariffs on the Emotion AI Market

President Trump's recent tariffs particularly the 145% duties on Chinese imports—have significantly impacted the Emotion AI market by disrupting the supply chains for critical AI hardware components. Companies like Nvidia, which rely on Chinese-manufactured chips and assembly facilities in Mexico, now face increased production costs and logistical challenges. These disruptions not only infla...

Emotion-Based Ad Targeting Market Outlook

According to our latest research, the global Emotion-Based Ad Targeting market size reached USD 1.72 billion in 2024, reflecting strong momentum driven by advancements in artificial intelligence and data analytics. The market is projected to grow at a robust CAGR of 18.4% from 2025 to 2033, reaching a forecasted value of USD 8.23 billion by the end of 2033. This remarkable growth is fueled by the increasing demand for hyper-personalized advertising experiences, leveraging emotional insights to maximize consumer engagement and campaign effectiveness.

The primary growth driver for the Emotion-Based Ad Targeting market is the rapidly evolving landscape of digital advertising, where brands and agencies continuously strive to enhance campaign relevance and resonance. Traditional demographic and behavioral targeting methods are increasingly being supplemented, or even replaced, by emotion-based strategies that utilize real-time emotional analytics to tailor ad content. As competition intensifies across digital channels, advertisers are recognizing the superior ROI delivered by campaigns that adapt dynamically to users’ emotional states. This shift is supported by the proliferation of advanced sensors, computer vision, and natural language processing technologies, which enable accurate and scalable emotion detection across various touchpoints.

Another crucial factor boosting the growth of the Emotion-Based Ad Targeting market is the surge in consumer data availability and the sophistication of AI-powered analytics platforms. With the widespread adoption of smartphones, wearables, and IoT devices, advertisers now have access to unprecedented volumes of multimodal data, including facial expressions, voice intonations, and physiological signals. This data, when processed through machine learning and deep learning models, allows for nuanced understanding of consumer sentiments and intent. The integration of emotion analytics into programmatic advertising platforms ensures that ad creatives and placements are optimized not just for clicks, but for emotional impact and brand affinity, driving higher conversion rates and long-term customer loyalty.

Additionally, the regulatory landscape and evolving consumer expectations around data privacy are shaping the trajectory of the Emotion-Based Ad Targeting market. While concerns about intrusive data practices persist, leading solution providers are investing heavily in privacy-preserving technologies such as federated learning and on-device processing. These innovations enable advertisers to harness emotional insights without compromising user trust or breaching compliance standards such as GDPR and CCPA. As a result, brands are increasingly able to deploy emotion-based targeting strategies that are both effective and ethical, further accelerating market adoption across diverse industry verticals.

Regionally, North America continues to dominate the Emotion-Based Ad Targeting market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of global technology giants, advanced digital infrastructure, and a mature advertising ecosystem have positioned North America at the forefront of innovation and adoption. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, expanding internet penetration, and a burgeoning e-commerce landscape. Europe, with its stringent data privacy regulations, is witnessing steady growth as companies balance innovation with compliance. Latin America and the Middle East & Africa are also expected to witness increased adoption, albeit at a more gradual pace, as digital transformation initiatives gain traction.

Component Analysis

The Emotion-Based Ad Targeting market is segmented by component into Software and Services, each playing a critical role in the overall ecosystem. The software segment encompasses emotion analytics platforms, SDKs, APIs, and integration tools that enable advertisers and agencies to capture, process, and interpret emotional cues from consumers in real time. These solutions leverage cutting-edge AI algorithms, computer vision, and natural language processing to analyze facial expressions, voice modulations, and other biometric indicators. The continuous evolution of these software solutions, with features such as multi-modal analytics and cross-channel integration, is a m

The data were obtained through an experience sampling method (ESM) survey of 71 adults (33 males and 38 females, ages 21-67) living in Japan. Exkuma software (https://exkuma.com) was used to record the response timing and collect the response data. A pre-survey was conducted on the first day of the survey (Day 1). Respondents reported their gender, age, household income, and subjective economic status. Individual differences in gain-approach/loss-avoidance orientations were measured using 14 items from the Japanese version of the Promotion/Prevention Focus Scale (PPFS-J; Ozaki & Karasawa, 2011). From the second day of the survey, a 7-day ESM survey was conducted (Days 2-8). As a survey procedure, four signals were sent to participants' smartphones each day at random times between 9:00 a.m. and 9:00 p.m. to prompt them to respond to an online survey. Participants rated their emotional state at the time of response using a 7-point scale, and then reported on their recent economic behaviors (consumption or saving) within the hour prior to the beginning of the response. The variables comprising the data are as follows: SignalDate (Date and time of signal transmission), SignalSeconds (Time from transmission to responding in seconds), SignalOrder (Signal order, range: 1-28), StartDate (Start date and time of response), FinishDate (Finish date and time of response), SurveySeconds (Duration of response in seconds), id (ID number of participants), e_prom_pos (Rating of promotion-related positive emotion “cheerful”, range: 1-7), e_prev_pos (Rating of prevention-related positive emotion “calm”, range: 1-7), e_prom_neg (Rating of promotion-related negative emotion “dejected”, range: 1-7), e_prev_neg (Rating of prevention-related negative emotion “agitated”, range: 1-7), done_c (Recent consumption behavior; yes = 1, no = 0), done_s (Recent saving behavior; yes = 1, no = 0), age (Age), gender (Gender; male = 1, female = -1, no response = 0), income (Annual income in millions of yen; No response = .), economic_s (Subjective economic status, range: 1-7), prom_scale (Gain-approach orientation, range: 1-7), prev_scale (Loss-aversion orientation, range: 1-7), age_m_c (Age [grand-mean centered]), income_m_c (Income [grand-mean centered]; No response was converted to “0”), prom_m_c (Gain-approach orientation [grand-mean centered]). prev_m_c (Loss-aversion orientation [grand-mean centered]), economic_m_c (Subjective economic status [grand-mean centered]).

The Emotion Detection And Recognition Market report segments the industry into By Software And Services (Software, Services), By End-User Vertical (Government, Healthcare, Retail, Entertainment, Transportation, Other End-User Verticals), and By Geography (North America, Europe, Asia Pacific, Latin America, Middle East & Africa, Rest Of The World). Get five years of historical data alongside five-year market forecasts.

The Emotion Detection and Recognition market is rapidly evolving as organizations increasingly prioritize understanding human emotions to enhance user experiences and interactions. This market focuses on the technologies and processes that enable the identification and analysis of emotional expressions in individual

Snapshot img

Emotion Recognition and Sentiment Analysis Software Market Size 2024-2028

The emotion recognition and sentiment analysis software market size is forecast to increase by USD 797.17 million at a CAGR of 14.15% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of wearable devices and the adoption of real-time sensing analysis. These technologies enable more accurate and timely emotion recognition, providing valuable insights for various applications, including healthcare, marketing, and customer service. However, the market faces challenges, most notably the issue of low-quality video content hampering emotional interpretation. Regulatory hurdles also impact adoption, as organizations navigate complex data privacy and security regulations.
To capitalize on market opportunities and navigate challenges effectively, companies must focus on improving data quality, investing in advanced algorithms, and addressing regulatory requirements. By doing so, they can differentiate themselves in a competitive landscape and drive innovation in the market.

What will be the Size of the Emotion Recognition and Sentiment Analysis Software Market during the forecast period?

Request Free Sample

The market is experiencing significant growth, driven by the increasing adoption of conversational AI and virtual assistants. This technology enables the analysis of both textual and multimedia data, including audio and video, to extract emotional insights from user interactions. Data mining techniques, such as predictive modeling and model deployment, play a crucial role in processing and interpreting this data. Sentiment analysis dashboards and emotion recognition dashboards provide valuable insights into user experience, allowing businesses to map and optimize both the employee and customer journey. Cognitive computing and cognitive AI technologies are also integral to this market, enabling real-time analysis of user behavior and feedback.
Data ethics and responsible AI are becoming increasingly important considerations in this market, with a focus on data governance and model training to ensure accurate and explainable AI. Biometric data and behavioral data are also being leveraged to enhance the capabilities of emotion recognition systems, further expanding their applications. Model evaluation and model training are essential components of this market, ensuring the accuracy and effectiveness of AI models. Interpretable AI and explainable AI are also gaining traction, enabling businesses to understand the reasoning behind AI decisions and build trust in the technology. Data annotation and data annotation tools are critical for training AI models, ensuring high-quality data and accurate sentiment analysis.
Overall, the market is poised for continued growth, offering businesses valuable insights into user emotions and improving the user experience.

How is this Emotion Recognition and Sentiment Analysis Software Industry segmented?

The emotion recognition and sentiment analysis software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Customer service/experience
  Product/market research
  Patient diagnosis
  Others


Deployment

  On-premises
  Cloud-based


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Application Insights

The customer service/experience segment is estimated to witness significant growth during the forecast period.

Emotion AI technology, integrated with sentiment analysis tools, is revolutionizing business operations by enabling real-time understanding of customer emotions and feedback. These solutions utilize machine learning, natural language processing, and computer vision to analyze text, voice, and facial expressions for sentiment scoring, emotion classification, and polarity analysis. Emotion lexicons and sentiment lexicons are used to identify and categorize emotions, while deep learning and predictive analytics provide insights into historical trends. Sentiment analysis plays a crucial role in various industries, including human resources for employee engagement and feedback analysis, fraud detection, and brand reputation management. It is also used in customer service to enhance customer experience through personalized communication and proactive issue resolution.

Social media monitoring and text analysis help businesses stay updated on brand mentions and customer sentiments, while voice analysis and tone analysis provide valuable insights from customer interactions. Integration with APIs, cloud computing, and data visualization tools streamlines the process, allowing for seamless implem

In 2023, the Emotion Sensing System Market reached a value of USD 19.27 billion, and it is projected to surge to USD 44.78 billion by 2030.

1) Data Introduction • The PRDECT-ID: Indonesian Emotion dataset is a collection of Indonesian product reviews annotated with emotion and sentiment labels, gathered from Tokopedia. It includes various attributes such as product name, location, price, overall rating, number sold, total reviews, customer rating, customer review, sentiment, and emotion.

2) Data Utilization (1) PRDECT-ID: Indonesian Emotion data has characteristics that: • It includes detailed product reviews annotated with sentiment (positive or negative) and emotion (love, happiness, anger, fear, sadness). This allows for comprehensive analysis of customer sentiments and emotional responses to different products. (2) PRDECT-ID: Indonesian Emotion data can be used to: • Sentiment and Emotion Analysis: Helps in understanding customer satisfaction and emotional reactions, aiding businesses in improving product quality and customer service. • Marketing and Product Development: Assists in developing targeted marketing strategies and improving products based on customer feedback and emotional responses.

Computer Vision Scientist-Collected Dataset:This facial emotion recognition dataset has been meticulously curated by computer vision scientists using mobile phone cameras to capture candid moments of individuals expressing a spectrum of emotions, including Happy, Sad, Fear, and Humor. The dataset comprises a rich collection of images with diverse angles and backgrounds, providing a realistic portrayal of human emotional expression.Marketing Expert-Collected Dataset:The ethnicity-focused dataset for facial recognition has been meticulously assembled by marketing experts, aiming to shed light on the vital aspect of ethnicity variations in computer vision. With a dedicated focus on ethnicity, this dataset provides a unique perspective for training and testing facial recognition models in an ethnically diverse context.This dataset comprises a rich collection of images capturing individuals from various ethnic backgrounds. It emphasises the importance of ethnicity in the field of computer vision and includes a wide range of facial features, expressions, and poses, thereby enriching the dataset's diversity.By offering insights into the critical area of ethnicity in computer vision, this dataset is a valuable addition to the toolkit of researchers and practitioners, facilitating the development of more inclusive and accurate facial recognition models.Researchers and experts in the fields of computer vision and marketing are encouraged to explore these datasets for their research, model development, and the advancement of understanding in these respective domains.

The Social Emotional Learning (SEL) Platform market is experiencing significant growth as the focus on emotional intelligence and mental health continues to rise in educational settings and beyond. SEL platforms are designed to foster social, emotional, and cognitive skills, empowering students to navigate their emo

As the intersection of technology and mental health grows increasingly relevant in today's society, the Emotional Therapy Robots market has emerged as a pivotal segment, offering innovative solutions for emotional support and mental well-being. Emotional therapy robots, equipped with artificial intelligence and adva

The Emotions Analytics (EA) Software market is experiencing robust growth, driven by increasing demand for personalized customer experiences, advancements in artificial intelligence (AI) and machine learning (ML), and the rising adoption of digital channels across various industries. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, businesses are leveraging EA to gain deeper insights into consumer behavior, enabling more effective marketing strategies, product development, and customer service improvements. Secondly, the sophistication of EA technology continues to improve, with more accurate emotion detection capabilities and the integration of diverse data sources (facial expressions, voice tone, text analysis) resulting in more comprehensive and reliable insights. Finally, growing regulatory requirements concerning data privacy and ethical considerations are driving demand for robust and compliant EA solutions. However, the market's growth is not without its challenges. High initial investment costs for implementing EA systems and the need for specialized expertise to interpret and analyze the collected data can act as significant barriers to entry for smaller businesses. Moreover, concerns surrounding data privacy and the potential for misuse of emotionally sensitive information remain important hurdles that need to be addressed through transparent data handling practices and robust ethical guidelines. The competitive landscape is characterized by a mix of large established technology firms like Microsoft and IBM, alongside innovative specialized companies like iMotions and Affectiva, fostering a dynamic market environment with varied technological approaches and service offerings. Future growth will depend on continued technological advancements, the development of robust ethical frameworks, and increased awareness of the value proposition of EA across diverse sectors.

This is a corpus of mentions (posts, tweets, retweets, articles, comments to public articles, social media posts and comments, forum discussion comments) concerning three reputational crisis peaks in the Polish language. It also contains mention classification with respect to emotions, topics and communicative actions.

The Emotional Management Software market is rapidly gaining traction as organizations increasingly recognize the significance of emotional intelligence in fostering a productive work environment. This innovative software serves as a vital tool for individuals and businesses aiming to enhance emotional wellbeing, imp