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Erik Cambria Amir Hussain a Common-Sense-Based Framework Socio-Aff ective Computing 1 Erik Cambria Amir Hussain Sentic Computing A Common-Sense-Based Framework for Concept-Level Sentiment Analysis Socio-Affective Computing Volume 1 Series Editor Amir Hussain, University of Stirling, Stirling, UK Co Editor Erik Cambria, Nanyang Technological University, Singapore This exciting Book Series aims to publish state-of-the-art research on socially intelligent, affective and multimodal human-machine interaction and systems. It will emphasize the role of affect in social interactions and the humanistic side of affective computing by promoting publications at the cross-roads between engineering and human sciences (including biological, social and cultural aspects of human life). Three broad domains of social and affective computing will be covered by the book series: (1) social computing, (2) affective computing, and (3) interplay of the first two domains (for example, augmenting social interaction through affective computing). Examples of the first domain will include but not limited to: all types of social interactions that contribute to the meaning, interest and richness of our daily life, for example, information produced by a group of people used to provide or enhance the functioning of a system. Examples of the second domain will include, but not limited to: computational and psychological models of emotions, bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning etc.). This series will publish works of the highest quality that advance the understanding and practical application of social and affective computing techniques. Research monographs, introductory and advanced level textbooks, volume editions and proceedings will be considered. More information about this series at http://www.springer.com/series/13199 Erik Cambria • Amir Hussain Sentic Computing A Common-Sense-Based Framework for Concept-Level Sentiment Analysis Source is: www.sentic.net 123 Copyrights Erik Cambria, Amir Hussain Erik Cambria Amir Hussain School of Computer Engineering Computing Science and Mathematics Nanyang Technological University University of Stirling Singapore, Singapore Stirling, UK Socio-Affective Computing ISBN 978-3-319-23653-7 ISBN 978-3-319-23654-4 (eBook) DOI 10.1007/978-3-319-23654-4 Library of Congress Control Number: 2015950064 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com) In memory of Dustin, A man with a great mind and a big heart. Foreword It was a particular joy to me having been asked to write a few words for this second book on sentic computing - the first book published in 2012, gave me immense inspiration which has gripped me ever since. This also makes it a relatively easy bet that it will continue its way to a standard reference that will help change the way we approach sentiment, emotion, and affect in natural language processing and beyond. While approaches to integrate emotional aspects in natural language understand- ing date back to the early 1980s such as in Dyer’s work on In-Depth Understanding, at the very turn of the last millennium, there was still very limited literature in this direction. It was about 3 years after Picard’s 1997 field-defining book on Affective Computing and one more after the first paper on Recognizing Emotion in Speech by Dellaert, Polzin, and Waibel and a similar one by Cowie and Douglas-Cowie that followed ground-laying work, including by Scherer and colleagues on vocal expression of emotion and earlier work on synthesizing emotion in speech when a global industrial player placed an order for a study whether we can enable computers to recognize users’ emotional factors in order to make human-computer dialogues more natural. After first attempts to grasp emotion from facial expression, our team realized that computer vision was not truly ready back then for “in the wild” processing. Thus, the thought came to mind to train our one-pass top-down natural language understanding engine to recognize emotion from speech instead. In doing so, I was left with two options: train the statistical language model or the acoustic model to recognize basic emotions rather than understand spoken content. I decided to do both and, alas, it worked – at least to some degree. However, when I presented this new ability, the usual audience response was, mainly along the lines of “Interesting, but what is the application?” Since then, a major change of mind has taken place: it is by and large agreed that taking into account emotions is key for natural language processing and understanding, especially for tasks such as sentiment analysis. As a consequence, these days, several hundred papers dealing with the topic ap- pear annually, and one finds several thousand citations each year in this field which is still gaining momentum and expected to be nothing less than a game-changing factor in addressing future computing challenges, such as when mining opinion, vii viii Foreword retrieving information, or interacting with technical systems. Hardly surprisingly, the commercial interest is ever rising, and first products-have already found their way into broad public awareness. The lion’s share of today’s work aimed at dealing with analyzing emotion and sentiment in spoken and written language, is based on statistical word co- occurrences. The principle is described in Joachim’s 1996 work on text catego- rization representing a document as a “bag-of-words” in a vector space. Different normalizations are named, and sequences of n words or characters (‘n-grams’) have since been applied successfully in similar fashion. With the advent of “big data,” recent approaches, such as by Google, translate the single words into (their individual) vectors by (some form of) soft clustering. This reflects each word’s relation to the other words in the vocabulary as added information. However, such approaches have reached a certain glass ceiling over the years as they are very limited in taking inspiration from how the human brain processes both emotions (by exploiting an emotion model) and meaning (by working at the semantic/concept level rather than at the syntactic/word level) to perform natural language processing tasks such as information extraction and sentiment analysis. This is what Sentic Computing is all about. Targeting the higher hanging fruits by not missing the importance of aiming to emulate the brain’s described processing of emotions and meaning, it provides a knowledge-based approach to concept-level sentiment analysis that is well rooted in a multi-disciplinary view. The consideration of a text as a bag-of-words is accordingly substituted by representing it as a “bag- of-concepts.” This embeds linguistics in an elegant form beyond mere statistics, enriching the representation of text by the dependency relation between clauses. The book guides its readers from the student-level onwards in an ingenious fashion, from an introduction and background knowledge (not only on sentiment analysis and opinion mining but also common sense) to the core piece – SenticNet (introducing the acquisition and representation of knowledge as well as reasoning) to concept- level sentiment analysis. It then exemplifies these ideas by three excellently picked applications in the domains of the social web, human-computer interaction, and e- health systems before concluding remarks. Thus, besides providing the essential comprehension of the basics of the field in a smooth and very enjoyable way, it not only manages to take the reader to the next level but introduces genuine novelty of utmost valuable inspiration to any expert in the field. In fact, it makes a major contribution to the next generation of emotionally intelligent computer systems. Do not be surprised catching yourself reasoning about sentiment and opinions in a whole new way even in your “non-tech” life. It remains to say that I am truly looking forward to the volumes to follow this one that kicks off the series on Socio- Affective Computing edited by the authors – and sets the bar utmost high for all aspiring readers. Imperial College, London, UK Björn W. Schuller July 2015 President, Association for the Advancement of Affective Computing (AAAC) Editor-in-Chief: IEEE Transactions
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