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02383nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8885 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251217152416.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251217b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9 A73 S47 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Servo, Samantha Vivien I.; Inso, Kelly Denise A. |
| 245 ## - TITLE STATEMENT |
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| Title |
Modified support vector machine algorithm for text classification applied psychiatric tele-triage |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2525 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (Bachelor of Science Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: This study investigates the use of Support Vector Machine (SVM) models to enhance text classification for tele-triage in psychiatry. The issue addressed is SVM’s tendency to ignore significant textual features, which results in low precision and recall, particularly in multi-class classification tasks with imbalanced classes. In order to address this, the researchers propose generating embeddings using the Large Language Model (LLM) RoBERTa, then reducing the dimensionality using PCA before training the SVM model. The dataset includes 500 Reddit posts with five categories of suicide risk: Attempt, Behavior, Ideation, Indicator and Supportive. Experts used the Columbia Suicide Severity Rating Scale (C-SSRS) to sort these posts. Results slow significant improvement over the baseline SVM model. The model initially had trouble with recall and precision, especially for the Attempt class, which had zero precision. Significant were observed in the Supportive class (precision: 0.55 to 0.59, recall: 0.43 to 0.57) and Behavior (precision: 0.25 to 0.31, recall: 0.13 to 0.27) following the implementation of the RoBERTa-based strategy. Even though the attempt demonstrated some improvement (precision: 0.00 to 0.33), more optimization is required. These results suggest that incorporating RoBERTa embeddings and PCA for dimensionality reduction can enhance SVM’s performance by preventing the loss of important features. The model still has issues with minority classes, suggesting that more research is needed to enhance recall for underrepresented categories and handle class imbalances. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
Thesis/Dissertation |