國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Investigations in computational sarcasm

Bhattacharyya, Pushpak.

Investigations in computational sarcasm

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigations in computational sarcasmby Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.
作者:	Joshi, Aditya.
其他作者:	Bhattacharyya, Pushpak.
出版者:	Singapore :Springer Singapore :2018.
面頁冊數:	xii, 143 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Engineering.
電子資源:	http://dx.doi.org/10.1007/978-981-10-8396-9
ISBN:	9789811083969$q(electronic bk.)

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.16..

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$q(electronic bk.)

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

210888
Engineering.

LC Class. No.: Q342 / .J674 2018

Dewey Class. No.: 006.35

Investigations in computational sarcasm
LDR:03199nmm a2200325 a 4500 001 533803
003 DE-He213
005 20180913113828.0
006 m d
007 cr nn 008maaau
008 181205s2018 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 809657
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 210888
650 0 $a Computational linguistics. $3 181250
650 0 $a Computational intelligence. $3 210824
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Language Translation and Linguistics. $3 274469
650 2 4 $a Signal, Image and Speech Processing. $3 273768
700 1 $a Bhattacharyya, Pushpak. $3 770780
700 1 $a Carman, Mark J. $3 809658
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Cognitive systems monographs ; $v v.16. $3 559859
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-8396-9
950 $a Engineering (Springer-11647)