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Investigations in Computational Sarcasm (Hardcover, 1st ed. 2018)
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Investigations in Computational Sarcasm (Hardcover, 1st ed. 2018)
Series: Cognitive Systems Monographs, 37
Expected to ship within 12 - 17 working days
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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.
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