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Investigations in Computational Sarcasm

  • Aditya Joshi
  • Pushpak Bhattacharyya
  • Mark J. Carman

Part of the Cognitive Systems Monographs book series (COSMOS, volume 37)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 1-31
  3. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 33-57
  4. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 59-91
  5. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 93-118
  6. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 119-127
  7. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman
    Pages 129-135
  8. Back Matter
    Pages 137-143

About this book

Introduction

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.

Keywords

Sentiment analysis Sarcasm detection sarcasm generation Irony markers Opinion mining Computational irony Automatic Identification of Sarcasm Recognition of sarcasm Identifying sarcasm cognitive linguistics of incongruity

Authors and affiliations

  • Aditya Joshi
    • 1
  • Pushpak Bhattacharyya
    • 2
  • Mark J. Carman
    • 3
  1. 1.IITB-Monash Research AcademyIndian Institute of Technology Bombay MumbaiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology-BombayMumbaiIndia
  3. 3.Faculty of Information TechnologyMonash UniversityMelbourneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-8396-9
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-10-8395-2
  • Online ISBN 978-981-10-8396-9
  • Series Print ISSN 1867-4925
  • Series Online ISSN 1867-4933
  • Buy this book on publisher's site
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