Machine And Deep Learning Techniques For Emotion Detection
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Machine and Deep Learning Techniques for Emotion Detection by Rai, Mritunjay,Pandey, Jay Kumar Pdf
Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.
Deep Learning Techniques Applied to Affective Computing by Zhen Cui,Wenming Zheng Pdf
Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.
Using Machine Learning to Detect Emotions and Predict Human Psychology by Rai, Mritunjay,Pandey, Jay Kumar Pdf
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies by Kyandoghere Kyamakya,Fadi Al-Machot,Ahmad Haj Mosa,Hamid Bouchachia,Jean Chamberlain Chedjou,Antoine Bagula Pdf
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies by Kyandoghere Kyamakya,Fadi Al-Machot,Ahmad Haj Mosa,Hamid Bouchachia,Jean Chamberlain Chedjou,Antoine Bagula Pdf
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.
Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence by Chowdhary, Chiranji Lal Pdf
Emotional intelligence has emerged as an important area of research in the artificial intelligence field as it covers a wide range of real-life domains. Though machines may never need all the emotional skills that people need, there is evidence to suggest that machines require at least some of these skills to appear intelligent when interacting with people. To understand how deep learning-based emotional intelligence can be applied and utilized across industries, further study on its opportunities and future directions is required. Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence explores artificial intelligence applications, such as machine and deep learning, in emotional intelligence and examines their use towards attaining emotional intelligence acceleration and augmentation. It provides research on tools used to simplify and streamline the formation of deep learning for system architects and designers. Covering topics such as data analytics, deep learning, knowledge management, and virtual emotional intelligence, this reference work is ideal for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
Machine Learning for Emotion Analysis in Python by Allan Ramsay,Tariq Ahmad Pdf
Kickstart your emotion analysis journey with this step-by-step guide to data science success Key Features Discover the inner workings of the end-to-end emotional analysis workflow Explore the use of various ML models to derive meaningful insights from data Hone your craft by building and tweaking complex emotion analysis models with practical projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionArtificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.What you will learn Distinguish between sentiment analysis and emotion analysis Master data preprocessing and ensure high-quality input Expand the use of data sources through data transformation Design models that employ cutting-edge deep learning techniques Discover how to tune your models’ hyperparameters Explore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use cases Practice your newly acquired skills by working on real-world scenarios Who this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.
Machine Learning for Face, Emotion, and Pain Recognition by Gholamreza Anbarjafari,Jelena Gorbova,Rain Eric Hammer,Pejman Rasti,Fatemeh Noroozi Pdf
This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures.
Machine Learning for Face, Emotion, and Pain Recognition by Gholamreza Anbarjafari,Jelena Gorbova,Rain Eric Hammer,Pejman Rasti,Fatemeh Noroozi Pdf
This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures.
Machine Learning for Face, Emotion, and Pain Recognition by Gholamreza Anbarjafari,Jelena Gorbova,Rain Eric Hammer,Pejman Rasti,Fatemeh Noroozi Pdf
This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures.
EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
This is a captivating book that delves into the intricacies of building a robust system for emotion detection in textual data. Throughout this immersive exploration, readers are introduced to the methodologies, challenges, and breakthroughs in accurately discerning the emotional context of text. The book begins by highlighting the importance of emotion detection in various domains such as social media analysis, customer sentiment evaluation, and psychological research. Understanding human emotions in text is shown to have a profound impact on decision-making processes and enhancing user experiences. Readers are then guided through the crucial stages of data preprocessing, where text is carefully cleaned, tokenized, and transformed into meaningful numerical representations using techniques like Count Vectorization, TF-IDF Vectorization, and Hashing Vectorization. Traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Convolutional Neural Network (CNN), are explored to provide a foundation for understanding the strengths and limitations of conventional approaches. However, the focus of the book shifts towards the Long Short-Term Memory (LSTM) model, a powerful variant of recurrent neural networks. Leveraging word embeddings, the LSTM model adeptly captures semantic relationships and long-term dependencies present in text, showcasing its potential in emotion detection. The LSTM model's exceptional performance is revealed, achieving an astounding accuracy of 86% on the test dataset. Its ability to grasp intricate emotional nuances ingrained in textual data is demonstrated, highlighting its effectiveness in capturing the rich tapestry of human emotions. In addition to the LSTM model, the book also explores the Convolutional Neural Network (CNN) model, which exhibits promising results with an accuracy of 85% on the test dataset. The CNN model excels in capturing local patterns and relationships within the text, providing valuable insights into emotion detection. To enhance usability, an intuitive training and predictive interface is developed, enabling users to train their own models on custom datasets and obtain real-time predictions for emotion detection. This interactive interface empowers users with flexibility and accessibility in utilizing the trained models. The book further delves into the performance comparison between the LSTM model and traditional machine learning models, consistently showcasing the LSTM model's superiority in capturing complex emotional patterns and contextual cues within text data. Future research directions are explored, including the integration of pre-trained language models such as BERT and GPT, ensemble techniques for further improvements, and the impact of different word embeddings on emotion detection. Practical applications of the developed system and models are discussed, ranging from sentiment analysis and social media monitoring to customer feedback analysis and psychological research. Accurate emotion detection unlocks valuable insights, empowering decision-making processes and fostering meaningful connections. In conclusion, this project encapsulates a transformative expedition into understanding human emotions in text. By harnessing the power of machine learning techniques, the book unlocks the potential for accurate emotion detection, empowering industries to make data-driven decisions, foster connections, and enhance user experiences. This book serves as a beacon for researchers, practitioners, and enthusiasts venturing into the captivating world of emotion detection in text.
This cutting-edge book brings together experts in the field to provide a multidimensional perspective on sentiment analysis, covering both foundational and advanced methodologies. Readers will gain insights into the latest natural language processing and machine learning techniques that power sentiment analysis, enabling the extraction of nuanced emotions from text. Key Features: •State-of-the-Art Techniques: Explore the most recent advancements in sentiment analysis, from deep learning approaches to sentiment lexicons and beyond. •Real-World Applications: Dive into a wide range of applications, including social media monitoring, customer feedback analysis, and sentiment-driven decision-making. •Cross-Disciplinary Insights: Understand how sentiment analysis influences and is influenced by fields such as marketing, psychology, and finance. •Ethical and Privacy Considerations: Delve into the ethical challenges and privacy concerns inherent to sentiment analysis, with discussions on responsible AI usage. •Future Directions: Get a glimpse into the future of sentiment analysis, with discussions on emerging trends and unresolved challenges. This book is an essential resource for researchers, practitioners, and students in fields like natural language processing, machine learning, and data science. Whether you’re interested in understanding customer sentiment, monitoring social media trends, or advancing the state of the art, this book will equip you with the knowledge and tools you need to navigate the complex landscape of sentiment analysis.
Deep Learning-Based Approaches for Sentiment Analysis by Basant Agarwal,Richi Nayak,Namita Mittal,Srikanta Patnaik Pdf
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Intelligent Learning Techniques for Human Emotions by S Dolia Pdf
Humans typically express their emotions verbally or nonverbally as a response to an outside event. Several modalities, including text, audio, body motions, facial expressions, and physiological signs, can be used to conduct emotion recognition. The field of human emotion recognition is dynamic because it has many uses in human-computer interaction. The goal of the authors' work is to develop intelligent and adaptive learning algorithms for the recognition of human emotions from physiological signals, micro-expressions, and facial expressions. Using both posed and spontaneous facial expressions, precise and efficient deep learning models for the classification of human emotions have been described. For precise computational techniques, these models take advantage of the discrete wavelet transform and the self-attention mechanism. This book also demonstrates the widespread acceptance of transformer models in language processing tasks because of their exceptional performance. Hence, human emotion recognition through micro-expressions has been achieved in this work by utilizing a modified version of the existing vision transformer. Furthermore, using physiological inputs, specifically electroencephalograms (EEGs), a deep learning model for human emotion identification has been developed. This book primarily aims to accomplish two things: (i) to identify emotions from physiological patterns and facial expressions; and (ii) to develop deep learning frameworks that are intelligent and adaptive and solve the challenges associated with human emotion recognition.
Design of Intelligent Applications using Machine Learning and Deep Learning Techniques by Ramchandra Sharad Mangrulkar,Antonis Michalas,Narendra Shekokar,Meera Narvekar,Pallavi Vijay Chavan Pdf
Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL. · Comprehensive analyses and probabilistic approaches for ML and DL. · Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. · Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.
