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.

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.

This dataset includes 10,000 data user interactions with video advertisements and 300 categorized ad images from the ADS dataset. It contains demographic data, emotional reactions, watch time, click-through rates, and feedback ratings, making it ideal for research in ad personalization, emotion recognition, and multimedia analysis.

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

The datset is comprised of 46(22 commercial adertisement and 24 kannada Music clips) different subjcets EEG data recorded uisng 2 channel EEG device

The dataset folder contaions two sub folder 1. comercial advertisement 1.1 Channel_1(Ch_1) and Channel_2 (Ch_2) :Prefontal Cortex 2. Kannada Musical clips 2.1 channel_1(Ch_1) and Channel_2 (Ch_2) :left Brain

Excel file information : Each file column represneted as number of subjects and row is represnted as features per subjects There are totaly 12 excel files from two channels ( 6 for commercial advertisemnt and 6 for kannda Musical clips).

Subjective self-rating scale

Name
age Gender Have you ever had any health issues? YES NO Have you watched this song/advertisement before? YES NO Please let us know if this advertisement brings up any specific memories for you. YES NO Please Rate the following query from 1 to 10. How funny was the advertisement you watched How sad was the advertisement you watched How Horror was the advertisement you watched How relaxed was the Music you viewed with How Sad was the Music you viewed with How enjoyable was the Music you viewed with Do you think what you just watched was entertaining enough? If you have any comment please write here

Here is the website address for each stimulus that we considered:

ad1: https://www.youtube.com/watch?v=ZzG7duipQ7U&ab_channel=perfettiindia ad2: https://www.youtube.com/watch?v=SfAxUpeVhCg&ab_channel=bo0fhead ad3: https://www.youtube.com/watch?v=HqGsT6VM8Vg&ab_channel=kiddlestix song1: https://www.youtube.com/hashtag/kgfchapter2 song 2: https://www.youtube.com/watch?v=x43w4lLS9E0&ab_channel=AnandAudio Song 3: https://youtube.com/watch?v=Ysf4QRrcLGM&si=EnSIkaIECMiOmarE

For a more comprehensive understanding of the dataset and its background, we kindly ask researchers to refer to our associated manuscript titled:

Entertainment Based Database for Emotion Recognition from EEG Signals, the research article accepted at 3rd International Conference on Applied Intelligence and informatics (AII2023) held in Fostering reproducibility of research results right 29 -31 OCT 2023, DUBAI, UAE. (When utilizing this dataset in your research, please consider citing the following reference)

The uploaded data set and do files accompany the paper Liza von Grafenstein, Sarah Iweala, Stefan Pahl, Anette Ruml, Emotional priming for sustainable consumption? The effects of social media content on the valuation of chocolate, Q Open, Volume 5, Issue 1, 2025, qoaf003, https://doi.org/10.1093/qopen/qoaf003

According to a survey conducted among U.S. consumers who viewed ** ads of Super Bowl LV in February 2021, ** percent of respondents found Toyota's "Upstream" ad to be emotionally engaging. Ads by Jeep, Brass Pro Shops, NFL were all considered emotionally engaging by ** percent of respondents, while ** percent cited Door Dash's "The Neighborhood" ad.

In this paper, we examine the ways in which citizens emotionally react to and cognitively process campaign advertisements that contain group-based appeals. Specifically, we focus on the emotional, cognitive, and persuasive effects of three campaign ads aired during the 2012 election campaign that contained explicit appeals to women voters. We analyze differences across women and men in their emotional responses to the ads, in their reports of the memorability of the ads, in their cognitive engagement with the ads, and in how persuasive the ads were for vote choice. In so doing, we add nuance to studies of gender and campaigns and contribute to the expanding literature on the impact of strategic campaign communications.

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 study investigates how social media usage, engagement behavior, and emotional connection collectively influence consumer–brand relationships within the digital fashion industry. Using a quantitative, correlational research design, survey data from 414 active social media users across India were analyzed through descriptive statistics, correlation analysis, and Principal Component Analysis (PCA). The results reveal three distinct yet interrelated dimensions—Social Media Usage, Social Media Engagement, and Brand Intimacy—explaining over 54% of total variance. Findings demonstrate that engagement significantly mediates the relationship between social media usage and brand intimacy, indicating that consistent, interactive, and personalized content enhances emotional trust and loyalty. The study contributes theoretically by affirming engagement as a multidimensional construct encompassing behavioral and emotional elements, and by extending relationship marketing and social exchange theory in digital contexts. From an industry perspective, it emphasizes that authenticity, personalization, and transparency are key to fostering sustained engagement and emotional attachment. These insights provide a strategic framework for fashion brands to design human-centered, data-informed, and emotionally resonant digital marketing strategies that transform visibility into long-term brand intimacy and consumer loyalty.

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.

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Emotion AI Market Size 2025-2029

The emotion AI market size is forecast to increase by USD 11.43 billion at a CAGR of 23.8% between 2024 and 2029.

The market is experiencing significant growth as businesses increasingly prioritize hyper-personalization and enhanced customer experience. This trend is driven by the rising demand for human-like interactions in various sectors, including marketing, healthcare, and education. Emotion lexicons and sentiment lexicons are used to identify and categorize emotions, while deep learning and predictive analytics provide insights into historical trends. Furthermore, the convergence of generative AI and emotion AI is leading to a paradigm shift towards relational technology, enabling more nuanced and effective communication between machines and humans. However, ethical, privacy, and regulatory hurdles pose significant challenges.
Additionally, navigating complex regulatory landscapes, particularly in areas such as data protection and AI ethics, is essential for market success. Companies seeking to capitalize on these opportunities must stay abreast of emerging trends and address these challenges effectively to succeed in the market. However, the market faces challenges, most notably the issue of low-quality video content hampering emotional interpretation. As AI systems become increasingly sophisticated, ensuring they respect user privacy and adhere to ethical standards is crucial.

What will be the Size of the Emotion AI Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market encompasses various applications, including education and training, healthcare monitoring systems, and customer service improvement. One innovative application is the Fatigue Detection System, which utilizes emotion-aware user interfaces to identify signs of exhaustion in students or employees. Assistive technologies, such as Speech Emotion Recognition, provide psychological assessment and mental health applications, enhancing emotional well-being. Market research applications leverage AI-driven emotional insights for brand reputation management and personalized marketing strategies. In the healthcare sector, stress detection systems and risk assessment technology contribute to improved patient care. Automotive safety systems employ emotion classification models to ensure driver safety and comfort.

Social media analysis and image emotion detection are essential tools for human resource management and security and surveillance. Adaptive user experiences in gaming and entertainment create engaging experiences, while emotion data annotation fuels the development of more accurate emotion AI models. Predictive emotional modeling and brand reputation management are crucial for businesses seeking to understand their customers' emotional responses. Emotion AI is revolutionizing industries, from education and healthcare to customer service and marketing, by providing valuable emotional insights. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives.

How is this Emotion AI Industry segmented?

The emotion AI industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Video
  Voice-focused
  Multimodal
  Text-focused


Technology

  Machine learning
  Natural language processing
  Others


Component

  Software
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The Video segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements in human-computer interaction through natural language processing and multimodal emotion sensing. Emotional intelligence metrics and real-time emotion detection are integral components, enabling contextual emotion understanding and predicting emotional responses. AI model explainability ensures transparency, while the generalizability of models allows for behavioral pattern recognition and sentiment analysis algorithms. Biometric authentication and data security measures ensure data privacy and protection. Facial expression tracking via computer vision techniques plays a crucial role, with systems interpreting subtle movements using the Facial Action Coding System (FACS). Voice tone analysis and text sentiment detection further enhance emotion recognition cap

Description

The dataset provided in this study serves as a helpful resource for comprehending emotional reactions within the domain of neuromarketing. Data was acquired from a sample of 58 participants, ranging in age from 18 to 70, through the implementation of a carefully planned experimental design. The participants were shown a collection of 35 branding advertisements classified into three categories: cosmetics and fashion, car and technology, and food and market. The Empatica e4 wearable sensor device was utilized to record several physiological signals, such as photoplethysmography (PPG), electrodermal activity (EDA), and body temperature. Concurrently, the process of capturing facial expressions was conducted using high-definition cameras during periods dedicated to viewing advertisements. The emotional assessments of the participants were evaluated utilizing an emotion appraisal scale, while demographic information was gathered via questionnaires. The utilization of this multifaceted dataset allows scholars to explore the complex realm of consumer decision-making processes, taking into account variables such as age, gender, and varied cultural backgrounds. Through the integration of physiological signals and facial expressions, the dataset offers valuable insights into the underlying neurological mechanisms that drive emotional responses toward branded commercials. This can be utilized by researchers to study emotional patterns, investigate correlations between consumers and advertising, and develop customized neuromarketing methods that are beneficial for individual preferences.

NeuroBioSense Dataset

Published: 20 December 2023 | Version 2 | DOI: 10.17632/7md7yty9sk.2

Dataset Overview:

This dataset provides valuable insights into emotional responses within the context of neuromarketing, based on data collected from 58 participants aged 18-70. Participants viewed 35 branding advertisements across three categories:

• Cosmetics & Fashion
• Car & Technology
• Food & Market

Data Collection:

The data was collected using the Empatica e4 wearable sensor to measure physiological signals such as:

• Photoplethysmography (PPG)
• Electrodermal Activity (EDA)
• Body Temperature

Simultaneously, high-definition cameras captured facial expressions during the advertisement viewing periods. The emotional reactions of the participants were assessed using an emotion appraisal scale, and demographic information was gathered through questionnaires.

Key Features:

• Physiological Data: Signals like PPG, EDA, and body temperature for emotional analysis.
• Facial Expression Data: Captured during advertisement viewing to assess emotional reactions.
• Demographic Information: Age, gender, and cultural background of participants.
• Emotion Appraisal: Evaluations of emotional responses to advertisements.

Applications:

• Neuromarketing research
• Studying consumer decision-making and emotional patterns
• Developing personalized advertising strategies
• Exploring correlations between emotions, consumer preferences, and demographics

This dataset is particularly useful for researchers looking to understand emotional patterns, identify emotional triggers in advertising, and explore how consumer preferences can be influenced by physiological and facial expression data.

Citation

NeuroBioSense Dataset: Kocaçınar, B., İnan, P., Zamur, E. N., Çalşimşek, B., Akbulut, F. P., & Catal, C. (2024). NeuroBioSense: A multidimensional dataset for neuromarketing analysis. Data in Brief, 53, 110235.

Description

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.

The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.

Citing the RAVDESS

The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.

Academic paper citation

Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391.

Personal use citation

Include a link to this Zenodo page - https://zenodo.org/record/1188976

Commercial Licenses

Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.

Contact Information

If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Example Videos

Watch a sample of the RAVDESS speech and song videos.

Emotion Classification Users

If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Construction and Validation

Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.

The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from PLoS ONE.

Contents

Audio-only files

Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):

• Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
• Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

Audio-Visual and Video-only files

Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:

• Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.
• Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.

File Summary

In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).

File naming convention

Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:

Filename identifiers

• Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
• Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

Filename example: 02-01-06-01-02-01-12.mp4

1. Video-only (02)
2. Speech (01)
3. Fearful (06)
4. Normal intensity (01)
5. Statement "dogs" (02)
6. 1st Repetition (01)
7. 12th Actor (12)
8. Female, as the actor ID number is even.

License information

The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0

Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.

Related Data sets

• RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Descriptive statistics on ADS.

On the basis of the cognitive–affective–behavioral model, this study investigated the effects of narrative transportation in movies on audience emotion and positive word-of-mouth.

1) Data Introduction • The Color associations in Art: Pt 1 Warmth Dataset is a color/emotional association dataset that provides information on whether or not the color is warm (1) along with various color HEX, RGB, and HSV codes, and in a tabular format on the emotion or characteristics associated with the color.

2) Data Utilization (1) Color associations in Art: Pt 1 Warmth Dataset has characteristics that: • Each color includes 'warm' (binary label) along with various color codes such as unique ID, HEX, RGB, HSV, and the emotions or characteristics commonly associated with that color (e.g., stability, sadness, passion, etc.). • Some colors are separated into different shades (e.g., navy blue, sky blue, etc.) within the same series, even reflecting differences in emotional relevance due to color change. (2) Color associations in Art: Pt 1 Warmth Dataset can be used to: • Development of color classification and emotion prediction models: Using various color codes and emotion labels, AI can build a model that automatically classifies and predicts the warmth/coldness and associative emotions of colors. • Use art, design and marketing: Based on color-specific emotional relevance data, it can be applied to establish strategies that take into account color psychology and emotional responses in works of art, brand design, and advertising.

The Emotion Analytics market is experiencing robust growth, projected to reach $3.90 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 15.93% from 2025 to 2033. This significant expansion is driven by several key factors. Increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries is a major catalyst, enabling more accurate and efficient emotion recognition from various data sources like facial expressions, voice tone, and text. The rising need for personalized customer experiences across sectors such as marketing, healthcare, and human resources is fueling demand for emotion analytics solutions that provide valuable insights into consumer behavior and employee well-being. Furthermore, advancements in sensor technology and the availability of large datasets for training sophisticated emotion recognition algorithms are contributing to market growth. The market's growth trajectory is also shaped by the increasing focus on mental health and the development of applications that leverage emotion analytics for early detection and intervention of mental health conditions. Despite this positive outlook, the market faces certain challenges. Data privacy concerns and ethical considerations surrounding the use of emotion recognition technology represent significant hurdles. The need for robust data security measures and transparent data handling practices is crucial to build consumer trust and avoid regulatory scrutiny. Furthermore, the accuracy and reliability of emotion analytics solutions can vary depending on factors like cultural context, individual differences, and the quality of data used for training. Addressing these challenges and ensuring responsible development and deployment of emotion analytics technologies will be key to realizing the market's full potential. The market is segmented by technology (e.g., facial expression analysis, voice tone analysis, text analysis), application (e.g., customer experience, healthcare, security), and region. Leading players like Affectiva Inc, IBM Corporation, and Clarifai Inc are actively shaping market innovation and competition. Recent developments include: Sept 2023: Affectiva, a Smart Eye company, announced a new attention metric in its cloud-based Emotion AI offering. This metric, bolstered by Smart Eye's advanced automotive safety-grade eye-tracking technology, represents a significant development in viewer attention measurement for brands, advertisers, entertainment companies, and their market researchers. The unique and comprehensive measurement analyzes gaze and head position, delivering unparalleled accuracy and insights., May 2023: Apple Inc. announced the introduction of an AI-powered healthcare app named Quartz that will be featured in the upcoming Apple Watch and iPhone models. Apple is adding tools for tracking emotions and managing vision conditions such as nearsightedness to the health app this year. The emotion tracker’s initial version will focus on collecting user data to create a database. Users will be able to log their moods, answer questions about their day, and monitor the results over time.. Key drivers for this market are: Growing IoT Applications and Adoption of Wearable Devices, Increasing Need for Advanced Marketing Tools. Potential restraints include: Growing IoT Applications and Adoption of Wearable Devices, Increasing Need for Advanced Marketing Tools. Notable trends are: Speech Analytics to Hold a Significant Share in the Emotion Analytics Market.

Facial Emotion Recognition Faces Dataset

The dataset consists of images capturing people displaying 7 distinct emotions (anger, contempt, disgust, fear, happiness, sadness and surprise). Each image in the dataset represents one of these specific emotions, enabling researchers and machine learning practitioners to study and develop models for emotion recognition and analysis.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

The images encompass a diverse range of individuals, including different genders, ethnicities, and age groups. The dataset aims to provide a comprehensive representation of human emotions, allowing for a wide range of use cases.

The dataset's possible applications:

• automatic emotion detection
• mental health analysis
• artificial intelligence (AI) and computer vision
• entertainment industries
• advertising and market research
• security and surveillance

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2Fe72fc2820f1452bcdc99b4bc69e4c7b0%2FMacBook%20Air%20-%201.png?generation=1689578335866939&alt=media" alt="">

Content

• images: includes folders corresponding to people and containing images with 8 different impersonated emotions, each file is named according to the expressed emotion
• .csv file: contains information about people in the dataset

Emotions in the dataset:

• anger
• contempt
• disgust
• fear
• happy
• sad
• surprised

File with the extension .csv

includes the following information for each set of media files:

• set_id: id of the set of images,
• gender: gender of the person,
• age: age of the person,
• country: country of the person

Images for facial emotion recognition might be collected in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

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