Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

Emotional AI and Human-AI Interactions in Social Networking makes readers aware of recent progress in this integrated discipline. Filling the existing vacuum in research in artificial intelligence with the application of social science, this book provides in-depth knowledge of human-AI interactions with social networking and increased use of the internet. Chapters integrating Emotional Artificial Intelligence, examining behavioral interventions, compassion, education, and healthcare, as well as social cognitive networking, including social brain networks, play a pivotal role in enhancing interdisciplinary studies in the field of social neuroscience and Emotional AI. This volume is a must for those wanting to dive into this exciting field of social neuroscience AI.

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining. The book- • Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation. • Discusses recent advancements in language-independent and domain-independent information extraction from textual and/ or multimodal data. • Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining. • Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing. • Covers latest datasets for natural language processing and information retrieval for social media like Twitter. The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.