I-PAL: Mental Health Companion/ (Record no. 28377)

MARC details
000 -LEADER
fixed length control field 02843nam a22003377a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20231211160120.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231211b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency TUPM
Language of cataloging eng
Transcribing agency -
Modifying agency -
Description conventions rda
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number BTH TK 7816
Item number A45 2019
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Aliman, George B.
245 ## - TITLE STATEMENT
Title I-PAL: Mental Health Companion/
Statement of responsibility, etc. Tanya Faye S. Nivera and and four others
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Manila:
Name of producer, publisher, distributor, manufacturer Technological University of the Philippines,
Date of production, publication, distribution, manufacture, or copyright notice 2020.
300 ## - PHYSICAL DESCRIPTION
Extent 141 pages :
Other physical details illustrations ;
Dimensions 30 cm.
336 ## - CONTENT TYPE
Source rdacontent
337 ## - MEDIA TYPE
Source rdamedia
338 ## - CARRIER TYPE
Source rdacarrier
500 ## - GENERAL NOTE
General note Thesis (Undergraduate)
502 ## - DISSERTATION NOTE
Dissertation note College of Engineering--
Degree type Bachelor of Science in Electronics Engineering,
Name of granting institution Technological University of the Philippines,
Year degree granted 2020.
520 3# - SUMMARY, ETC.
Summary, etc. Social media is without a doubt a place where most people express their thoughts and feelings. Among the various social media currently there is, Twitter is where people are more vocal than in personal. A result from a study conducted by Child Mind Institute suggests that the more time you spent using social media, more likely you are depressed.<br/>Also, in recent years, cases of deaths relating to mental illness have become alarming, taking account for affected adults of 19%, 36% for adolescents and 16% for young kid.<br/>Given these facts, this paper proposed a model of sentiment analysis for predicting signs of depression from a Tweet (post from Twitter) using Natural Language Processing (NLP) and Logistic Regression. The said model will be integrated to Twitter's API and will stream for Tweets using APT search. Also, the sentiment analysis employed is applicable to English, Filipino and Taglish (Combination of English and Filipino language) languages.<br/>The system is capable of appeasing the emotions of identified mentally crisis<br/>Twitter users by sending motivational and uplifting quotes for the first reply. The bot will also intervene the user by sending mental health helplines or links for the second reply.<br/>The data of classified users with mental health crisis will be emailed to the mental health<br/>experts<br/>Based on the series of tests, the efficiency in predicting alarming tweets concerning mental health has increased due to the added training data and hit words to<br/>the system. The probability that the system could detect and reply alarming tweets<br/>concerning mental health was 92% based on Psychologist validation.
Expansion of summary note Author's Abstract
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Natural language processing (Computer science)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Human-computer interaction
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Programming languages (Electronic computers)
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Aliman, George B.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Nivera, Tanya Faye S.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Olazo, Jensine Charmille A
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ramos, Daisy Jane P.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Sanchez, Chris Danielle B.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Bachelor's Thesis COE
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Inventory number Total checkouts Full call number Barcode Date last seen Price effective from Koha item type Public note
    Library of Congress Classification     TUP Manila Library TUP Manila Library Thesis Section-2nd floor 08/31/2020 BTH-3468   BTH TK 7816 A45 2019 BTH0003468 12/11/2023 08/31/2020 Bachelor's Thesis COE For room use only



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