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020 _a9789811083969
_9978-981-10-8396-9
024 7 _a10.1007/978-981-10-8396-9
_2doi
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_2bicssc
072 7 _aTEC009000
_2bisacsh
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082 0 4 _a006.3
_223
100 1 _aJoshi, Aditya.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_953407
245 1 0 _aInvestigations in Computational Sarcasm
_h[electronic resource] /
_cby Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2018.
300 _aXII, 143 p. 12 illus., 4 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCognitive Systems Monographs,
_x1867-4933 ;
_v37
505 0 _a1. Introduction -- 2. Literature Survey -- 3. Understanding the Phenomenon of Sarcasm -- 4. Sarcasm Detection using Incongruity within Target Text -- 5. Sarcasm Detection using Contextual Incongruity -- 6. Sarcasm Generation -- 7. Conclusion & Future Work.
520 _aThis 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.
650 0 _aComputational intelligence.
_97716
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aSignal processing.
_94052
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aSignal, Speech and Image Processing .
_931566
700 1 _aBhattacharyya, Pushpak.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_953408
700 1 _aCarman, Mark J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_953409
710 2 _aSpringerLink (Online service)
_953410
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811083952
776 0 8 _iPrinted edition:
_z9789811083976
776 0 8 _iPrinted edition:
_z9789811341397
830 0 _aCognitive Systems Monographs,
_x1867-4933 ;
_v37
_953411
856 4 0 _uhttps://doi.org/10.1007/978-981-10-8396-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
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