Author(s): Tarun Gupta* and Supriya Bansal
ABSTRACT
This This research aims to comprehensively review the current state of artificial intelligence techniques for emotional recognition and their potential applications in optimizing digital advertising strategies. A systematic literature review was conducted involving searches of academic databases and screening of papers on topics relating to emotion recognition using facial analysis, sentiment analysis, computational advertising, and measuring digital engagement. Key studies developing AI models for multimodal emotion recognition from videos, images, and neurophysiological signals were analyzed. The report finds that while progress has been made in fields like facial emotion detection and sentiment analysis of social media data, limitations remain around data and context. It concludes that further work developing larger datasets, advancing multimodal approaches, and accounting for dynamic contexts could help realize the full benefits of emotion AI for personalized digital marketing.
In Emotional AI can be described as a specific area within the broader field of artificial intelligence [1]. Artificial intelligence generally involves machines that simulate how humans think and respond to instructions [2]. In contrast, emotional AI focuses on measuring, understanding, replicating, and responding to human emotions in different ways. It is also known as affective computing or artificial emotional intelligence [1].
Whereas general artificial intelligence aims to replicate human cognition, emotional AI narrows in on simulating our ability to experience emotions and interact emotionally. It detects how users are feeling using modalities like facial analysis, voice tone analysis and more. Then emotional AI aims to respond to users in a sensitive, emotionally intelligent manner tailored to their detected affective state.
Emotional recognition makes all this possible. Emotional recognition refers to the capability to accurately understand human feelings and sentiments from various inputs and channels [3]. Emotions can be deduced from responses to surveys, physical cues such as facial expressions and body language, as well as physiological indicators like heart rate, breathing, and brain activity [4]. Advances in technologies like computer vision, machine learning and neural networks are enabling more precise emotional detection from these sources of information.
This burgeoning field of emotional recognition has generated substantial interest in recent years due to its wide range of use cases and applications across various sectors [3]. And marketing represents a major area where emotional understanding is being tapped to gain deeper consumer insights. Powerful trends like escalating internet connectivity, mobile device proliferation, and time spent on digital platforms have turbocharged digital advertising and media in recent decades. Marketers now allocate larger budgets toward digital channels due to abilities like targeted placement and measurable return on spending.
Emotional recognition is very important because, generating and sustaining user engagement amid saturated environments poses challenges for advertisers.
To optimize effectiveness on these fronts, deeper audience understanding has become crucial. Traits beyond mere demographics are needed to forge meaningful connections. Emotions often unconsciously drive intentions, decisions and recall more than rational factors alone. As such, emotional recognition holds immense potential value when integrated into digital advertising approaches. Innovations in computer vision, natural language processing and related AI domains now facilitate sentiment analysis of audiences at scale.
This research report aims to comprehensively explore the emerging area of AI-powered emotional recognition applied in digital marketing contexts. Specifically, it will outline techniques being engineered for automatic emotion categorization from multimedia content like videos and text. Opportunities and challenges of utilizing these advanced systems for personalized targeting and optimized user experiences will also be discussed. The goal is to provide in- depth insights into the current state and ongoing evolution of this consequential technology intersection. Promising directions, open questions and implications for the future of interactive digital advertising will likewise be examined.
The For this study, we conducted a review and analysis of existing academic literature on the topics of emotional recognition using AI techniques and measuring consumer engagement with digital advertising. Our research methodology involved the following steps:
We performed searches of academic databases like ACM Digital Library, IEEE Xplore, and SpringerLink to identify peer- reviewed articles related to emotional recognition, facial analysis, multimodal fusion, and measuring digital ad engagement.
We independently screened article titles, abstracts, and full texts to assess relevance and identify studies for inclusion in the review. Any discrepancies were resolved through discussion.
Key details were extracted from the included studies using a standardized form, including information on emotion recognition methods, datasets used, engagement metrics analyzed, results, and conclusions.
We analyzed the extracted data to identify trends, similarities, and differences in approaches across studies. Particular attention was paid to novel methods and promising results.
The literature was evaluated to determine what questions remain unanswered and where opportunities exist to advance the field through new primary research approaches.
By synthesizing the literature review and gap analysis, we developed conclusions on the current state of the field and proposed directions for future research into applying emotional recognition to optimize digital advertising engagement.
This systematic secondary research approach provided an in-depth overview of academic work in this emerging area to help identify opportunities for new primary studies.
Several studies have examined the effect of ad-induced emotions on consumer behavior [5,6]. Emotions sparked by advertisements significantly influence consumers’ brand perceptions [5]. Feelings evoked by ads impact users both openly and surreptitiously [6]. Ad-elicited emotions were found to alter consumer attitudes toward inherently gratifying products particularly [7].
By studying facial expressions, voice tone, or text emotion, companies can customize their marketing tactics to better connect with their intended audiences. This data-led process enables more customized customer communications, thereby potentially strengthening participation and perhaps increasing sales [8-10].
The face is an extremely important means of non- verbal communication between people. Facial expressions can convey a wide range of meaningful information beyond just words. They give hints about a person’s emotions, intentions, level of alertness, whether they are experiencing pain, and aspects of their personality [11]. Facial cues also help to govern social behaviors during interactions and indicate someone’s psychological or medical condition [11].
In the last decade and a half, there has been growing fascination from computer scientists and AI researchers to automatically analyze facial expressions through computer vision and machine learning techniques [11]. Being able to interpret the subtle signals conveyed by the face would offer valuable insights. It could provide insight into how people are feeling, how engaged they are, their attitudes, and much more. This interest has spurred progress in developing technologies for automated facial expression recognition.
Advances in artificial intelligence, specifically face recognition technologies, allow for more accurate analysis of consumer emotions in response to digital advertisements [11]. AI systems can leverage facial expression analysis through tools like Smart Face, Face tales, and id3 to gain deep insights into how viewers emotionally engage with online ads [11].
Neuromarketing combined with AI provides an effective way for marketers to understand the subconscious reactions and decision- making processes triggered by digital content. By capturing and analyzing consumer facial expressions through ad exposure experiments, AI helps reveal valuable emotions like fear, anger, or joy towards the product, branding, or messaging.
Emotion recognition technology has huge potential to notably improve how personalized customer experiences are, particularly in retail settings [13]. One of the most creative uses is emotion- aware digital signs and ads within stores [13]. These systems utilize facial recognition to evaluate the mood of shoppers as they look around. Relying on this real-time emotional data, the digital displays can alter what they show [13]. For example, if someone seems overwhelmed or stressed, it may spontaneously change to propose a calming tea or relaxation item. This degree of personalization aims to make each shopper’s time more pertinent and involving, ultimately boosting the chances of a sale [13].
Zhang et al. developed an artificial intelligence- powered system for analyzing video advertisements at a more granular, object level. Their goal was to gain insights into how certain elements of the ads elicited emotional responses from viewers [14].
They used a type of deep learning model called a deep convolutional neural network which is well-suited for computer vision tasks like image and facial recognition. The DCNN was specifically trained on facial features to detect and distinguish human emotions displayed in reactions to the ad content [14].
By recognizing facial cues like smiles, frowns, eye movements, etc., the AI system was able to predict the positive, negative or neutral sentiment or feelings evoked by different objects, scenes, or characters portrayed in the video ads. This provided an emotionally driven analysis beyond just looking at overall ad performance [14]. In addition, a heuristic algorithm was implemented to help optimize and make sense of the large dataset of emotional responses detected across many ad viewings. It helped determine which specific ad elements like graphics or messaging most strongly influenced viewer sentiment [14].
Image recognition combined with artificial intelligence (AI) is being used increasingly in brick-and-mortar retail stores [15]. While e-commerce has grown, the majority of purchases still occur in physical stores [16]. Retailers are now leveraging computer vision and AI-enabled image recognition systems to improve the customer experience and boost sales [15].
One such retailer leveraging this technology is Cloverleaf, based in San Diego. They have developed an “intelligent shelf-display” platform using optical sensors connected to an AI-powered image recognition system [15].
As shoppers stand in front of the display, the AI analyzes images captured by the sensors. Using deep learning algorithms, the image recognition model can detect objects like human faces [15]. It then applies advanced artificial neural networks to interpret facial features, expressions and micro-gestures [15].
Through this AI-enabled computer vision, the system can automatically infer useful customer data such as demographics like age and gender. Remarkably, it can also gauge a shopper’s emotional reactions to products displayed [15]. The AI model has learned to associate subtle emotional cues in visual imagery with feelings like joy, interest or disappointment.
The shelf display is then able to provide more tailored, personalized content to each shopper based on their proximity, as determined using AI object detection in additional photos. This level of real- time AI-powered personalization was previously only achievable through complex online customer profiles.
By integrating AI-driven image recognition, Cloverleaf has brought a personalized, data-rich in-store experience normally reserved for e-commerce. This reflects how AI is enhancing computer vision systems to transform physical retail operations.
The research by McDuff et al. conducted a large- scale analysis of facial reactions to online video advertisements to better understand how emotional recognition data relates to ad effectiveness metrics [17]. The researchers collected over 12,000 facial responses from viewers watching 170 ads across different product categories and markets. Facial expressions were automatically coded frame by frame using computer vision techniques, allowing them to analyze over 3.7 million facial frames on a scale not possible with traditional study methods. Their findings revealed expressions in individual frames tended to be infrequent, but cumulative responses over time uncovered rich “emotion trajectories”. By modeling the link between facial expressions and metrics like ad liking and intent to purchase changes, they found ad liking could be accurately predicted solely from facial data with a ROC AUC of 0.85. Purchase intent prediction was also feasible with a 0.78 ROC AUC. Specifically, positive expressions, especially when elicited just before showing the brand, correlated with increased effectiveness. Significantly, the system demonstrated facial responses could reliably foresee ad performance automatically without requiring self-reported user data, providing valuable clues into optimizing emotional responses and the essence of effective ads.
The studies reviewed demonstrate the progress that has been made in developing artificial intelligence and computer vision techniques for automated facial expression recognition and analysis. Systems that can interpret subtle emotional cues from viewers’ faces offer valuable insights into how consumers emotionally engage with and respond to digital advertising content. By capturing facial data through experiments, AI helps reveal emotions like fear, anger, or joy towards products, branding or messaging. This neuromarketing approach combined with emotion recognition technology provides an effective way for marketers to gain a deeper understanding of subconscious reactions and decision-making processes. The research also shows how AI is being applied in retail settings through digital displays and shelf systems that can personalize the customer experience in real time based on facial analysis. Overall, facial expression recognition using artificial intelligence represents a novel and promising approach for gaining actionable consumer insights to improve the engagement and effectiveness of digital ads. Further refinements and large-scale analyses continue advancing this important area of emotion AI.
Voice analysis is an important part of emotion recognition systems [13]. This process concentrates on the qualities of speech such as pitch, tone, and pace to deduce emotional states [13]. Machine learning models like support vector machines or recurrent neural networks are regularly employed to examine these qualities [13].
Researchers have used artificial intelligence (AI) and machine learning to perform voice tone analysis of Donald Trump’s tweets [18]. Specifically, they used IBM Watson Tone Analyzer to analyze the tone of over 35,000 tweets posted on Trump’s @ realDonaldTrump Twitter account from 2009 to 2018.
IBM Watson Tone Analyzer is an AI-powered service that can detect emotional and language tones in written text. It uses linguistic analysis and considers context to identify things like sentiment, emotions, social tendencies, and writing style present in a given text [18].
For this study, the researchers fed each of Trump’s tweets into the IBM Watson Tone Analyzer API. The AI tool then analyzed the language and emojis used in each tweet and assigned it a dominant tone classification of either “confident,” “analytical,” “tentative,” “sad,” “fear,” “angry” or “joyful.” It also quantified the intensity levels for each tone on a scale from 0 to 1 [18].
By applying AI-based tone analysis, the researchers were able to systematically categorize the emotional and language tone of over 35,000 tweets posted by Trump over a nearly 10-year period. This allowed them to study how variations in Trump’s social media voice, as detected through AI, correlated with different levels of engagement from his followers on Twitter [18].
The findings offer relevant insights for the use of emotional AI in marketing and engagement on social media. The results showed certain emotional tones were more effective than others at driving follower interactions. For example, tweets with negative emotions like anger produced higher engagement.
This suggests marketing professionals could leverage similar emotional AI systems to analyze brand messages and quantify the emotional impact. Understanding which emotional tones resonate most with target audiences could help optimize social media strategies and maximize user engagement. The study provides an example of how emotional AI can be applied to social listening and improve marketing communication effectiveness.
However, the voice tone analysis technology is impacted by background noise and people’s normal vocal traits, which may not precisely mirror how they are feeling emotionally [19].
The studies presented demonstrate the growing application of artificial intelligence techniques for voice tone analysis in determining emotional states from speech patterns. Tools like IBM Watson Tone Analyzer leverage machine learning to systematically categorize the linguistic and emotional tones present in written text, as seen through the analysis of Trump’s tweets. Applying such AI systems to analyze brand messages on social media can provide useful insights into how variations in communication styles and emotional tones may drive different levels of user engagement. However, voice analysis technology is still limited by background noise and individual vocal traits that do not always precisely correlate with felt emotions. Overall, while challenges remain, emotion AI shows promise for social listening and optimizing marketing communication strategies to better resonate with target audiences when combined with textual sentiment analysis approaches. Further refinements continue to advance this multimodal area examining how emotion is conveyed through both text and speech.
Text analysis algorithms can evaluate customer reviews and comments to determine feelings regarding particular products or services. This data can then be used to refine product descriptions, modify pricing tactics, or even guide inventory choices. The aim is to generate a more reactive and flexible retail setting that constantly progresses depending on customer sentiment [20].
According to Camberia, emotions play a pivotal role in understanding human preferences and the process of emotion processing [21]. To gain insights into consumer emotions, artificial intelligence can be leveraged to conduct sentiment analysis on textual data [22]. Sentiment analysis involves detecting the polarity or favorability of opinions expressed by consumers through language [23]. As social media usage continues to grow rapidly, vast amounts of unstructured user-generated content is being produced daily on platforms [24]. This large, complex data is known as “big data” and computational algorithms and artificial intelligence are needed to make sense of it and glean meaningful insights [24]. Through deep learning techniques, AI can uncover deeper patterns and layers of sentiment within massive datasets. When applied to social media data, AI enables the detection of polarizing consumer sentiments and emotions expressed towards issues, products, and brands [22]. The user- generated content shared by consumers on social networking sites provides a rich source of real-world inputs about actual consumer preferences, opinions, and decision- making processes [22]. By analyzing the emotions and sentiments within social media data using AI, businesses can develop a deep understanding of their customers to help improve strategic and tactical decisions related to areas such as marketing, product development, and pricing strategies.
For AI to truly understand human emotions, it needs capabilities like natural language processing and sentiment analysis. Poria et al. developed a model that combined computational methods with linguistic and emotive algorithms tailored for social media language [25]. This approach was applied to the large volumes of unstructured data generated daily on social platforms. By analyzing posts and conversations, the AI system was able to detect positive or negative sentiment and gauge overall polarity. Crucially, it assessed polarity through both the contextual route, examining how feelings were influenced by discussions around ads, and the content route, looking at direct reactions to advertising messages and creatives. By decoding sentiments through these two lenses, the AI provided realistic insights into how digital ads were dynamically shifting consumer perceptions in real- world social environments over time. This gave advertisers a deeper understanding of campaign influence beyond simple click- through metrics. Poria et al. demonstrated how artificial emotional intelligence can uncover nuanced perspectives on ad effectiveness from social media data [25]. Their work highlighted the potential for AI to meaningfully analyze online word-of-mouth and glean actionable sentiment analytics to optimize future digital marketing strategies.
Marketers utilize emotional artificial intelligence (AI) to gain insight into what consumers are saying about their brands publicly and how those consumers feel. This is done by analyzing consumer commentary on platforms like reviews, blogs, or videos [15]. Emotional AI is also used by marketers to pre-test advertisements before rolling them out widely [15].
For example, Kellogg’s employed Affectiva’s emotional AI software to help create an advertising campaign for their Crunchy Nut cereal brand [15]. The AI tool analyzed viewer engagement levels when potential advertisements were tested. Any ad executions that showed a decrease in viewer engagement after multiple viewings were removed from consideration by Kellogg’s [15]. This helped the company identify advertisements most likely to resonate positively with target audiences based on emotive responses analyzed through AI [15].
The literature reviewed demonstrates how artificial intelligence, through techniques like natural language processing and sentiment analysis, can be effectively applied to analyze large volumes of unstructured text-based consumer data. By detecting the underlying emotions, opinions, and attitudes expressed in online reviews, social media posts, and conversations, AI provides valuable insights into customer preferences, perceptions of brands/products over time, and responses to marketing campaigns. Researchers have developed multimodal approaches combining sentiment analysis with contextual and emotive linguistics to gain a nuanced understanding of how digital advertising dynamically shifts consumer perspectives on social platforms. Practitioners have also leveraged emotional AI tools to pre-test ads based on viewer engagement patterns in response to emotive content. Overall, AI shows promise for augmenting traditional analytics by unlocking deeper layers of sentiment within vast datasets to optimize strategic marketing decisions relating to areas such as advertising strategy and product development. Further advances continue to refine computational methods for detecting human emotion through social and conversational language.
Shukla et al. leveraged different artificial intelligence techniques for recognizing emotions conveyed by video advertisements and optimizing how ads are placed through computational advertising models. To analyze emotions at both the content and viewer level, they developed content-centric models that extracted visual and audio features from ads using convolutional neural networks, which are effective for image and audio classification. However, since their ad dataset was small, they fine-tuned the CNNs on a larger movie emotion dataset to help the models better learn emotional representations from ads. Interestingly, they also recorded EEG brain signals from participants as they viewed and rated the ads, and trained separate CNN models directly on these user- centric physiological recordings. The EEG-based models turned out to perform even better at emotion recognition compared to the content-only approach.
Additionally, realizing that similar ads may elicit similar emotions, they applied multi-task learning techniques to jointly learn related emotion recognition tasks and leverage any shared patterns between emotionally similar ads for improved performance Shukla et al, [26].
With robust emotion prediction capabilities, they were then able to carefully match emotional attributes between video scenes and ads to select the most contextually relevant ads and optimal insertion points that would maximize viewer engagement, recall of the ads, and overall watching experience.
User study evaluation confirmed that ads selected and placed through the EEG-based emotion recognition led to demonstrably better outcomes than traditional rule-based or manual methods, underscoring the value of AI in computational advertising and digital marketing applications Shukla et al, [26].
Shukla et al, compiled a dataset of emotional ads by having experts and volunteers judge which ads reliably evoked emotions [7]. They then tested using convolutional neural networks (CNNs) to detect emotions from ads. CNNs analyze visual patterns in images or videos - the researchers wanted to see if CNNs could recognize emotions. They compared CNNs to other existing methods of detecting emotions from visuals and sounds. Through multiple experiments, CNNs performed better at identifying emotions from ads. With an improved method of detecting ad emotions, the researchers developed a way to match ads to online videos based on predicted emotions. This allowed ads to be targeted based on the predicted feelings of the video content [7].
To evaluate their system, 17 volunteers viewed video clips with targeted ads inserted. The volunteers reported their viewing experience after. Results showed the targeted emotional ad placement led to a better viewing experience compared to random ad selection [7].
In sum, the researchers developed artificial intelligence techniques for understanding emotions in ads. Experiments proved CNNs work well for this task. A user study then confirmed their targeted emotional ad system enhanced how people experienced ad- interrupted videos. This validation shows emotion AI can benefit real-world digital advertising.
A study by Shukla looked at using AI like deep learning for emotional recognition in advertisement videos [27]. Specifically, they gathered a dataset of 100 ads labeled with emotions by experts to analyze how ads elicit feelings. Convolutional neural networks were used with transfer learning on visual and audio features to do “content- based” emotional recognition from the ads, outperforming existing methods. Brain wave data from users watching ads was also analyzed to do “user-based” emotional spotting, finding these models outperformed content-based ones. Audiovisual CNN features were combined with brain wave features using decision fusion, showing the multi-modal way to recognize emotions best. Multi-task learning was applied further to enhance recognition by taking advantage of connections between data types. As a real-world validation, emotional predictions were used in a computational advertising system, inserting ads at emotionally relevant points in videos. User studies revealed this markedly outperformed other techniques. In summary, numerous AI strategies for emotional recognition in ads were developed and evaluated from both content and user perspectives. Integrating these approaches achieved state-of- the-art results and proved helpful for targeted computational advertising when applied in a real system.
The studies presented demonstrate the application of various artificial intelligence techniques for multi-modal emotion recognition in digital advertising content. Researchers leveraged CNNs and transfer learning to effectively classify emotions from visual and audio features of ads. Capturing neurophysiological signals like EEG provided additional user-centric data to directly model emotional responses, improving recognition performance. Combining audiovisual and brain wave features through decision fusion achieved state-of-the-art results. Models were also enhanced through multi-task learning by leveraging shared patterns between related emotion classification tasks. To evaluate the real- world value of emotion AI, targeted computational advertising systems were developed that selectively inserted ads at emotionally salient points, as determined by emotion predictions. User evaluations confirmed these systems led to better viewing experiences than non-targeted approaches. Overall, the literature validated artificial intelligence as a promising tool for computational advertising, showing its potential to optimize ad placement based on dynamic emotional context. Further advances continue refining multi-modal approaches that integrate both content-based and human-based emotion data.
This research report makes a significant contribution by comprehensively surveying the current progress made in developing artificial intelligence techniques for emotional recognition across modalities like facial expressions, voice tones, text, and neurophysiological signals. It identifies major gaps still limiting the practical realization of benefits, such as a lack of standardized datasets and challenges inhibiting adoption like transparency, bias, and privacy concerns. The identification of these gaps and the proposal of novel directions to address them, particularly advancing multi-modal fusion modeling through end- to-end joint representation learning, represents important original insights that can help focus future work on problems most pertinent for propelling the field. By conducting a thorough analysis of the state-of-the-art and mapping the path ahead, this paper provides an invaluable resource to strategically drive the field of emotional AI toward achieving its fullest potential for optimizing digital experiences through deeper emotive understanding.
There are still several open challenges that need to be addressed by future work in this area. One major gap is the lack of large, standardized datasets that can be used to develop emotional recognition models. Most current studies analyze small, custom datasets, which limits the generalizability and real- world applicability of AI techniques. Additionally, accurately capturing the complex nature of human emotions remains difficult, as technologies like facial analysis and voice recognition have limitations and may not perfectly reflect subjective feelings in all cases. More research is also needed to develop multi-modal approaches that integrate different data sources, such as combining text, images, audio, and biometric signals to achieve more robust emotion detection. Capabilities for real- time, dynamic emotion tracking also require further advances beyond analyzing pre- recorded data offline. Contextual factors influencing emotions are understudied as well, and models seldom account for elements like culture, demographics, and personality. There is a lack of longitudinal evaluations assessing the long-term impacts of emotion-based advertising strategies on metrics like brand loyalty over extended periods. More work is also needed to facilitate adoption by the industry by addressing concerns in areas such as data privacy, potential biases, and system explainability. Ethical considerations surrounding these technologies must also be given careful attention to ensure their responsible and non-manipulative development and application.
While progress has been made in developing artificial intelligence techniques for emotion recognition, significant further advancement is still needed to fully realize the benefits of practical digital marketing applications at scale. One of the most prominent areas requiring attention is the need for larger, more varied, and standardized datasets that can be used to train emotion recognition models. The majority of current research relies on relatively small, limited datasets that are often collected through constrained lab studies or focus only on specific demographics. This restricts the generalizability of developed models, making it difficult to achieve robust and accurate emotion detection capabilities that translate well to realistic, diverse settings. Researchers must prioritize collecting substantially larger emotion-labeled datasets that involve more varied content, broader demographics, and cross-cultural representations sourced from real-world contexts. Establishing standardized benchmark datasets agreed upon across the research community would also allow for a more effective comparison of different techniques and evaluation of progress over time.
Another promising direction is further advancing multi- modal fusion techniques that aim to leverage diverse, synergistic data sources for emotion recognition. While initial explorations integrating modalities like text, images, audio, and physiological signals have shown promise, more sophisticated fusion architectures, and methodologies could potentially yield even more accurate emotion detection abilities by capturing the richness of human emotional expression through multiple integrated channels. For example, developing end-to-end models that jointly learn emotion representations across modalities as opposed to independent modeling may help uncover valuable cross- modality correlations. Researchers could also test novel fusion strategies like hierarchical fusion applying modality- specific sub-networks before integration or attention-based fusion highlighting the most relevant modalities. High- fidelity tracking of emotions requires optimized real-time capabilities rather than just offline analysis of static data - adaptive multi-modal architectures for continual emotional tracking remain an open challenge.
Beyond technical modeling issues, capturing how contextual factors influence emotions is another critical area demanding attention. Integrating demographic metadata alongside recognition models allows accounting for differences linked to factors such as age, gender, culture, and personality. This is important as such attributes meaningfully shape emotional responses yet remain largely unexplored. Furthermore, emotion recognition in practice requires accounting for dynamic contexts like social interactions, locations, and personal contexts that shift over time - modeling evolving contextual dependencies is non- trivial.
Another angle comprises addressing how emotional tendencies covary - sequential, temporal, or multi-task learning approaches could help leverage inter-relations. Addressing these contextual modeling challenges will be crucial for real-world applications requiring personalized, situation-aware emotional understanding.
Conducting rigorous, longitudinal validations through large- scale, real-deployment studies is also needed. While controlled experiments provide initial insights, the true impacts emerge only through long-term tracking of how emotion-aware applications influence crucial metrics. For example, in ads - does optimized emotional placement substantively impact brand awareness, loyalty, and purchase behavior over weeks/months rather than just immediate reactions? Marketers need such evidence to justify investments. Similarly, studies longitudinally tracking user experiences/engagement with services like intelligent retail aisles are needed. Beyond functional validations, proactively addressing ethical concerns will be paramount as applications grow - open issues around data privacy, the potential for bias, and designing non-manipulative experiences must be addressed through multidisciplinary collaborations between technologists, social scientists, and oversight bodies.
Facilitating adoption also requires overcoming barriers such as perceived lack of transparency, difficulty explaining emotion predictions, and concerns regarding privacy- preserving emotional modeling at scale. Interface challenges in integrating predictive capabilities into existing workflows and explaining emotion-driven recommendations also exist. Standardization efforts through open evaluation platforms and annotation toolkits can help streamline integration. Addressing explain ability is crucial to gain trust for high- stakes applications. Overall, balancing protection concerns while enabling insights will be key. Large-scale field validations in collaboration with industry partners gauging impacts on core metrics like sales, and footfall can complement lab studies and drive practical adoption [28].
This research report provided a comprehensive review of the current state of artificial intelligence techniques for emotional recognition and their emerging applications in optimizing digital advertising and marketing strategies. Specifically, it explored advancements in automatically analyzing emotions conveyed through facial expressions, voice tones, text, and multimodal data like videos and neurophysiological signals. The literature demonstrated the progress made in areas such as facial emotion recognition using deep learning models, sentiment analysis of social media data through natural language processing, and computational advertising systems that match ads to video content based on predicted viewer emotions.
However, several important research gaps remain that present opportunities for further study. Larger, more diverse benchmark datasets are needed to develop models with stronger generalizability. Capturing the complexity of human emotions accurately also remains challenging given the limitations of existing modalities. Greater integration of diverse data sources through advanced multi-modal fusion approaches could potentially yield more robust recognition abilities. Accounting for dynamic contextual factors shaping emotions is another understudied area requiring attention.
Additionally, more work is needed to conduct rigorous longitudinal validations of emotion-driven applications in real-world deployments and assess their impacts on metrics like brand loyalty over extended periods. Facilitating industry adoption also involves addressing concerns regarding transparency, potential biases, privacy considerations, and system explain ability. Proactively focusing on ethical issues will likewise be paramount as these technologies continue progressing.
Overall, while artificial emotional intelligence shows promising potential for automatically gaining actionable consumer insights and optimizing digital experiences, significant research challenges still exist that warrant further exploration through well-designed experiments and multidisciplinary collaborations. Continued advances in these important directions can help push the boundaries of this growing field and bring us closer to fully realizing the benefits of emotion AI within digital marketing contexts.
