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02629nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft8879 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251215162023.0 |
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251215b ||||| |||| 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.A43 G34 2024 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Alegria, Froilan M.; Bautista, Kurt John Adrianne C.; De Leon, Alfred Alwyn I. |
| 245 ## - TITLE STATEMENT |
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| Title |
An enhancement of fuzzy support vector machine using natural language processing model applied on online product reviews |
| 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 |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: The increasing complexity and volume of online product reviews pose significant challenges to traditional sentiment analysis methods, especially in multilingual and informal language contexts. Conventional Fuzzy Support Vector Machine (CFSVM) models demonstrate limitations in analyzing n on-English texts, understanding modern slang, and recognizing cultural nuances such as sarcasm and ambiguous expressions. This study addresses these shortcomings by enhancing FSVM through the integration of advanced Natural Language Processing (NLP) techniques, specifically BERT, DistilBERT, and Multilingual BERT (mBERT). The primary objective is to develop a sentiment analysis model capable of handling Filipino language input, neologisms, and culturally nuanced expression within product reviews. The researchers employed a hybrid methodology combining TF-IDF feature extraction, NLP-based language modeling, and a fuzzy inference system (FIS) that utilizes Gaussian membership functions and Bayesian rule sets to refine sentiment prediction. The model architecture was implemented using Python, PyCharm, and Streamlit, with testing performed on e-commerce review datasets. The results showed that the enhanced FSVM model outperformed traditional FSVM and other baseline approaches in terms of accuracy, adaptability, and interpretability. Specifically, it achieved better classification performance on multilingual and informal text data, effectively handling linguistic ambiguity and evolving language use. This enhancement contributes to the broader field of sentiment analysis by offering a robust, interpretable, and context-aware solution suitable for diverse linguistic environments, with practical implications for e-commerce platforms, marketing analysis, and natural language research. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
Thesis/Dissertation |