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This book describes the authors’ investigations of computational sarcasm based on the notion of incongruity. In addition, it provides a holistic view of past work in computational sarcasm and the challenges and opportunities that lie ahead. Sarcastic text is a peculiar form of sentiment expression and computational sarcasm refers to computational techniques that process sarcastic text. To first understand the phenomenon of sarcasm, three studies are conducted: (a) how is sarcasm annotation impacted when done by non-native annotators? (b) How is sarcasm annotation impacted when the task is to distinguish between sarcasm and irony? And (c) can targets of sarcasm be identified by humans and computers. Following these studies, the book proposes approaches for two research problems: sarcasm detection and sarcasm generation. To detect sarcasm, incongruity is captured in two ways: ‘intra-textual incongruity’ where the authors look at incongruity within the text to be classified (i.e., target text) and ‘context incongruity’ where the authors incorporate information outside the target text. These approaches use machine-learning techniques such as classifiers, topic models, sequence labelling, and word embeddings. These approaches operate at multiple levels: (a) sentiment incongruity (based on sentiment mixtures), (b) semantic incongruity (based on word embedding distance), (c) language model incongruity (based on unexpected language model), (d) author’s historical context (based on past text by the author), and (e) conversational context (based on cues from the conversation). In the second part of the book, the authors present the first known technique for sarcasm generation, which uses a template-based approach to generate a sarcastic response to user input. This book will prove to be a valuable resource for researchers working on sentiment analysis, especially as applied to automation in social media.
In recent years, there has been a proliferation of opinion-heavy texts on the Web: opinions of Internet users, comments on social networks, etc. Automating the synthesis of opinions has become crucial to gaining an overview on a given topic. Current automatic systems perform well on classifying the subjective or objective character of a document. However, classifications obtained from polarity analysis remain inconclusive, due to the algorithms' inability to understand the subtleties of human language. Automatic Detection of Irony presents, in three stages, a supervised learning approach to predicting whether a tweet is ironic or not. The book begins by analyzing some everyday examples of irony and presenting a reference corpus. It then develops an automatic irony detection model for French tweets that exploits semantic traits and extralinguistic context. Finally, it presents a study of portability in a multilingual framework (Italian, English, Arabic).
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
This book presents software engineering methods in the context of the intelligent systems. It discusses real-world problems and exploratory research describing novel approaches and applications of software engineering, software design and algorithms. The book constitutes the refereed proceedings of the Software Engineering Methods in Intelligent Algorithms Section of the 8th Computer Science On-line Conference 2019 (CSOC 2019), held on-line in April 2019.
As global communities are attempting to transform into more efficient and technologically-advanced metropolises, artificial intelligence (AI) has taken a firm grasp on various professional fields. Technology used in these industries is transforming by introducing intelligent techniques including machine learning, cognitive computing, and computer vision. This has raised significant attention among researchers and practitioners on the specific impact that these smart technologies have and what challenges remain. Applications of Artificial Intelligence for Smart Technology is a pivotal reference source that provides vital research on the implementation of advanced technological techniques in professional industries through the use of AI. While highlighting topics such as pattern recognition, computational imaging, and machine learning, this publication explores challenges that various fields currently face when applying these technologies and examines the future uses of AI. This book is ideally designed for researchers, developers, managers, academicians, analysts, students, and practitioners seeking current research on the involvement of AI in professional practices.
This book contains high quality research papers accepted and presented at the International Conference on Intelligent Computing, Communication and Information Security (ICICCIS 2022), organized by Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, India during 25-26, November 2022. It presents the solutions of issues and challenges in intelligent computing, communication and information security domains. This book provides a background to problem domains, considering the progress so far, assessing the potential of such approaches, and exploring possible future directions as a single readily accessible source.
A biography of two troublesome words. Isn't it ironic? Or is it? Never mind, I'm just being sarcastic (or am I?). Irony and sarcasm are two of the most misused, misapplied, and misunderstood words in our conversational lexicon. In this volume in the MIT Press Essential Knowledge series, psycholinguist Roger Kreuz offers an enlightening and concise overview of the life and times of these two terms, mapping their evolution from Greek philosophy and Roman rhetoric to modern literary criticism to emojis. Kreuz describes eight different ways that irony has been used through the centuries, proceeding from Socratic to dramatic to cosmic irony. He explains that verbal irony—irony as it is traditionally understood—refers to statements that mean something different (frequently the opposite) of what is literally intended, and defines sarcasm as a type of verbal irony. Kreuz outlines the prerequisites for irony and sarcasm (one of which is a shared frame of reference); clarifies what irony is not (coincidence, paradox, satire) and what it can be (among other things, a socially acceptable way to express hostility); recounts ways that people can signal their ironic intentions; and considers the difficulties of online irony. Finally, he wonders if, because irony refers to so many different phenomena, people may gradually stop using the word, with sarcasm taking over its verbal duties.
Industry 5.0 is poised to redefine the collaboration between humans and machines, marking a crucial moment in technological evolution. However, as we stand at the threshold of this transformative era, a critical challenge emerges – the integration of emotional intelligence into the industrial landscape. Organizations grapple with the urgent need to understand, strategize, and ethically deploy artificial emotional intelligence (AEI) in Industry 5.0. This pivotal juncture calls for a comprehensive resource that explores the theoretical foundations but offers practical insights into the applications, challenges, and responsible deployment of AEI. The absence of a cohesive guide addressing the intricacies of AEI in Industry 5.0 leaves a void in academic scholarship. Organizations, researchers, and policymakers lack a singular, authoritative source to navigate the complexities of emotional intelligence integration, impacting Industry 5.0 strategies, sustainability plans, and customer services. The challenge lies in managing the delicate balance between human and machine collaboration while ensuring ethical considerations are at the forefront of AI deployment. As the demand for emotional intelligence in the industrial landscape intensifies, the need for a unifying resource becomes increasingly apparent.