東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Investigations in computational sarcasm

Joshi, Aditya.

Linked to FindBook

Google Book

Amazon

博客來

Investigations in computational sarcasm

Record Type:	Electronic resources : Monograph/item
Title/Author:	Investigations in computational sarcasm/ by Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.
Author:	Joshi, Aditya.
other author:	Bhattacharyya, Pushpak.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	xii, 143 p. :ill., digital ;24 cm.
[NT 15003449]:	1. 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.
Contained By:	Springer eBooks
Subject:	Engineering. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-8396-9
ISBN:	9789811083969

Investigations in computational sarcasm
Joshi, Aditya.

Investigations in computational sarcasm[electronic resource] /by Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman. - Singapore :Springer Singapore :2018. - xii, 143 p. :ill., digital ;24 cm. - Cognitive systems monographs,v.371867-4925 ;. - Cognitive systems monographs ;v.37..

1. 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.

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.

ISBN: 9789811083969

Standard No.: 10.1007/978-981-10-8396-9doiSubjects--Topical Terms:

586835
Engineering.

LC Class. No.: Q342 / .J674 2018

Dewey Class. No.: 006.35

Investigations in computational sarcasm
LDR:03199nmm a2200325 a 4500 001 2136897
003 DE-He213
005 20180913113828.0
006 m d
007 cr nn 008maaau
008 181117s2018 si s 0 eng d
020 $a 9789811083969 $q (electronic bk.)
020 $a 9789811083952 $q (paper)
024 7 $a 10.1007/978-981-10-8396-9 $2 doi
035 $a 978-981-10-8396-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342 $b .J674 2018
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.35 $2 23
090 $a Q342 $b .J83 2018
100 1 $a Joshi, Aditya. $3 3308723
245 1 0 $a Investigations in computational sarcasm $h [electronic resource] / $c by Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2018.
300 $a xii, 143 p. : $b ill., digital ; $c 24 cm.
490 1 $a Cognitive systems monographs, $x 1867-4925 ; $v v.37
505 0 $a 1. 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 $a 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.
650 0 $a Engineering. $3 586835
650 0 $a Computational linguistics. $3 606540
650 0 $a Computational intelligence. $3 595739
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Language Translation and Linguistics. $3 892561
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Bhattacharyya, Pushpak. $3 3217984
700 1 $a Carman, Mark J. $3 3308724
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Cognitive systems monographs ; $v v.37. $3 3308725
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-8396-9
950 $a Engineering (Springer-11647)