Amir Hussain

The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain. The readers will discover the following key novelties: 1) development of a bio-medical lexicon: WME expansion and WME enrichment with additional features.; 2) ensemble of machine learning and computational creativity; 3) development of microtext analysis techniques to overcome the inconsistency in social communication. It will be of interest to researchers in the fields of socially-intelligent human-machine interaction and biomedical text mining

The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain. The readers will discover the following key novelties: 1) development of a bio-medical lexicon: WME expansion and WME enrichment with additional features.; 2) ensemble of machine learning and computational creativity; 3) development of microtext analysis techniques to overcome the inconsistency in social communication. It will be of interest to researchers in the fields of socially-intelligent human-machine interaction and biomedical text mining

On 9/11, many Americans were introduced to an Islamic movement called Salafism, the theological strand that includes Al Qaeda. Since then, Salafism, an important and popular movement in global Islam, has frequently been disparaged as 'Radical Islam' or 'Islamic fundamentalism.' Scripture People is the first book-length study of the embattled American Salafi movement and the challenges it has faced post-9/11. Matthew D. Taylor recounts how these so-called “Radical Muslims” have adopted deeply rooted American forms of religious belonging and values. Through comparison with American Evangelical Christianity, informed by his own Evangelical background and studies, Taylor explores the parallel impulses, convergent identities, and even surprising friendships that have emerged between Salafis and Evangelicals in America. Offering an entry point for understanding the dynamics and disagreements among American Muslims, Taylor's volume upends narratives about 'Radical Islam' by demonstrating how Salafi Muslims have flexibly adapted to American religious patterns in the twenty-first century.

On 9/11, many Americans were introduced to an Islamic movement called Salafism, the theological strand that includes Al Qaeda. Since then, Salafism, an important and popular movement in global Islam, has frequently been disparaged as 'Radical Islam' or 'Islamic fundamentalism.' Scripture People is the first book-length study of the embattled American Salafi movement and the challenges it has faced post-9/11. Matthew D. Taylor recounts how these so-called “Radical Muslims” have adopted deeply rooted American forms of religious belonging and values. Through comparison with American Evangelical Christianity, informed by his own Evangelical background and studies, Taylor explores the parallel impulses, convergent identities, and even surprising friendships that have emerged between Salafis and Evangelicals in America. Offering an entry point for understanding the dynamics and disagreements among American Muslims, Taylor's volume upends narratives about 'Radical Islam' by demonstrating how Salafi Muslims have flexibly adapted to American religious patterns in the twenty-first century.

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:• Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference• Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text• Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clausesThis volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.

This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.

This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.

There has never been an America without Muslims"" - so begins Amir Hussain, one of the most important scholars and teachers of Islam in America. Hussain, who is himself an American Muslim, contends that Muslims played an essential role in the creation and cultivation of the United States. Memories of 9/11 and the rise of global terrorism fuel concerns about American Muslims. The fear of American Muslims in part stems from the stereotype that all followers of Islam are violent extremists who want to overturn the American way of life. Inherent to this stereotype is the popular misconception that Islam is a new religion to America. In Muslims and the Making of America Hussain directly addresses both of these stereotypes. Far from undermining America, Islam and American Muslims have been, and continue to be, important threads in the fabric of American life. Hussain chronicles the history of Islam in America to underscore the valuable cultural influence of Muslims on American life. He then rivets attention on music, sports, and culture as key areas in which Muslims have shaped and transformed American identity. America, Hussain concludes, would not exist as it does today without the essential contributions made by its Muslim citizens.

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:• Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference• Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text• Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clausesThis volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.

This book presents a summary of the cognitively inspired basis behind multimodal speech enhancement, covering the relationship between audio and visual modalities in speech, as well as recent research into audiovisual speech correlation. A number of audiovisual speech filtering approaches that make use of this relationship are also discussed. A novel multimodal speech enhancement system, making use of both visual and audio information to filter speech, is presented, and this book explores the extension of this system with the use of fuzzy logic to demonstrate an initial implementation of an autonomous, adaptive, and context aware multimodal system. This work also discusses the challenges presented with regard to testing such a system, the limitations with many current audiovisual speech corpora, and discusses a suitable approach towards development of a corpus designed to test this novel, cognitively inspired, speech filtering system.

From disagreement over an Islamic Center in New York to clashes between Christians and Muslims in Egypt, tension between the three Abrahamic faiths often runs high. Yet for all their differences, these three traditions—Judaism, Islam, and Christianity—share much in common. Three Testaments brings together for the first time the text of the Torah, the New Testament, and the Quran, so that readers can explore for themselves the connections, as well as the points of departure, between the three faiths. Notable religion scholars provide accessible introductions to each tradition, and commentary from editor Brian Arthur Brown explores how the three faiths may draw similarities from the ancient Zoroastrian tradition. This powerful book provides a much-needed interfaith perspective on key sacred texts.

Complex Systems are made up of numerous interacting sub-components. Non-linear interactions of these components or agents give rise to emergent behavior observable at the global scale. Agent-based modeling and simulation is a proven paradigm which has previously been used for effective computational modeling of complex systems in various domains. Because of its popular use across different scientific domains, research in agent-based modeling has primarily been vertical in nature. The goal of this manuscript is to provide a single hands-on guide to developing cognitive agent-based models for the exploration of emergence across various types of complex systems. We present practical ideas and examples for researchers and practitioners for the building of agent-based models using a horizontal approach - applications are demonstrated in a number of exciting domains as diverse as wireless sensors networks, peer-to-peer networks, complex social systems, research networks, epidemiological HIV?

In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.