Название: Multimodal Affective Computing: Technologies and Applications in Learning EnvironmentsАвтор: Ramon Zatarain Cabada, Hector Manuel Cardenas Lopez
Издательство: Springer
Год: 2023
Страниц: 211
Язык: английский
Формат: pdf (true), epub
Размер: 21.0 MB
This book explores AI methodologies for the implementation of affective states in intelligent learning environments. Divided into four parts, Multimodal Affective Computing: Technologies and Applications in Learning Environments begins with an overview of Affective Computing and Intelligent Learning Environments, from their fundamentals and essential theoretical support up to their fusion and some successful practical applications. The basic concepts of Affective Computing, Machine Learning, and Pattern Recognition in Affective Computing, and Affective Learning Environments are presented in a comprehensive and easy-to-read manner. In the second part, a review on the emerging field of Sentiment Analysis for Learning Environments is introduced, including a systematic descriptive tour through topics such as building resources for sentiment detection, methods for data representation, designing and testing the classification models, and model integration into a learning system. The methodologies corresponding to Multimodal Recognition of Learning-Oriented Emotions are presented in the third part of the book, where topics such as building resources for emotion detection, methods for data representation, multimodal recognition systems, and multimodal emotion recognition in learning environments are presented. The fourth and last part of the book is devoted to a wide application field of the combination of methodologies, such as Automatic Personality Recognition, dealing with issues such as building resources for personality recognition, methods for data representation, personality recognition models, and multimodal personality recognition for affective computing.
Within important areas of greater and faster technological advances is Computer Science where Artificial Intelligence has become a generating branch of new fields of study that open windows that allow us to glimpse huge amounts of knowledge to be discovered to support the continuity of the progress of society as a whole. There are several branches of Artificial Intelligence that stand out for their contribution to the rapid development of intelligent systems, among those areas is Affective Computing which was initiated with the noble intention to “humanize” as much as possible the methodologies that take into account emotions, feelings, and personality to improve their functioning by providing capabilities that take into account the affective states inherent in the human race. This has spawned an emerging field of Artificial Intelligence known as Affective Computing (AC). On the other hand, another independent field was developing on its own particularly oriented to benefit all types of educational processes. Originally, this field was known as Intelligent Tutoring Systems (ITS), which after adopting new educational trends and tools became what now we know as Intelligent Learning Environments (ILE). The combination of these two methodologies has generated a synergistic symbiosis that in a very short time has demonstrated significant advances and benefits, and allowed the emergence of surprising fields of application previously not contemplated nor intuited. In this book entitled Multimodal Affective Computing, different Artificial Intelligence methodologies have been compiled that allow the implementation of affective states in intelligent learning environments. Inside the material provided by the authors, the reader will find a well-organized and detailed overview of the most relevant features of the two main methodologies from their fundamentals, their essential theoretical support up to their fusion and some successful practical applications. Basic concepts of Affective Computing, Machine Learning and Pattern Recognition in Affective Computing, and Affective Learning Environments are written in a comprehensive and easy to read manner.
For data collection, it was decided to use the social network Twitter to extract phrases related to the domain of interest. To perform this task, we used the Python language, and the Tweepy library, which allows us to use the Twitter API. Using the filtering and downloading functionalities of the Twitter development API, phrases with keywords such as programmer, developer, teacher, professor, student, Java, C#, Visual Basic, and Python, among others, were obtained.
Скачать Multimodal Affective Computing: Technologies and Applications in Learning Environments