國立虎尾科技大學 |

登入

回首頁

Classification of Social Media Data ...

Kulkarni, Purva Anand.

Classification of Social Media Data for Suicidal Ideation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Classification of Social Media Data for Suicidal Ideation./
作者:	Kulkarni, Purva Anand.
面頁冊數:	1 online resource (59 pages)
附註:	Source: Masters Abstracts International, Volume: 57-01.
Contained By:	Masters Abstracts International57-01(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355397567

Classification of Social Media Data for Suicidal Ideation.
Kulkarni, Purva Anand.

Classification of Social Media Data for Suicidal Ideation. - 1 online resource (59 pages)

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.S.)

Includes bibliographical references

Social media use continues to grow worldwide and an ever-growing number of people are using various social media platforms to update their social circles on their mental health challenges and suicidal ideations in real time. Number of psychological studies show that expressing suicidal thoughts and attempting suicide happens within a matter of hours and therefore automatic detection and analysis of social media posts by vulnerable users, serves as a critical, real-time window into their health and safety.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355397567Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Classification of Social Media Data for Suicidal Ideation.
LDR:02495ntm a2200373Ki 4500 001 910792
005 20180517112610.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355397567
035 $a (MiAaPQ)AAI10604412
035 $a (MiAaPQ)umbc:11681
035 $a AAI10604412
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Kulkarni, Purva Anand. $3 1182252
245 1 0 $a Classification of Social Media Data for Suicidal Ideation.
264 0 $c 2017
300 $a 1 online resource (59 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-01.
500 $a Adviser: Yelena Yesha.
502 $a Thesis (M.S.) $c University of Maryland, Baltimore County $d 2017.
504 $a Includes bibliographical references
520 $a Social media use continues to grow worldwide and an ever-growing number of people are using various social media platforms to update their social circles on their mental health challenges and suicidal ideations in real time. Number of psychological studies show that expressing suicidal thoughts and attempting suicide happens within a matter of hours and therefore automatic detection and analysis of social media posts by vulnerable users, serves as a critical, real-time window into their health and safety.
520 $a In this thesis, we are interested in classifying data from Twitter and Reddit as 'Suicidal Risky Expression' and 'Non-Risky Expression'. We propose a method which includes automatic collection of tweets (Twitter data) and posts (Reddit data) based on suicidal vocabulary, parsing and tokenizing collected textual data, passing this data through a trained neural network and segregating data into two classes 'Suicidal data' and 'Non-suicidal data'. Since this process runs in real time, the classified data can be used to support at-risk users either by reporting suicidal content to behavioral crisis response teams or by connecting people to mental-health support resources in real-time window.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Psychology. $3 555998
650 4 $a Web studies. $3 1148502
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0621
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, Baltimore County. $b Computer Science. $3 1179407
773 0 $t Masters Abstracts International $g 57-01(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10604412 $z click for full text (PQDT)

筆 0 讀者評論

多媒體

評論

新增評論分享你的心得

Export

取書館別