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An enhancement of fuzzy support vector machine applied to sentiment analysis in students faculty evaluation system ofPamantasan ng Lungsod ng Maynila. 6

By: Irish Dhesyrie I. Gagante, Mark Dave T. Limpin. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4541346Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: This study addresses fundamental challenges in sentiment analysis by enhancing the effectiveness of Fuzzy Support Vector Machines (FSVM). We propose novel methodologies to improve FSVM's interpretability, generality, and accuracy in sentiment classification. By integrating Bayesian rules, employing feature selection techniques, and optimizing fuzzy membership functions within a fuzzy logic frameqork, our approach aims to overcome the limitations of traditional FSVM. Through thorough testing and evaluation, our enhanced FSVM algorithm demonstrates superior adherence to predefined parameters, prioritizing precision and reliability in sentiment analysis tasks compares to existing methods. Specifically, we focus on refining feature selection to enhance model adaptability, incorporating fuzzy membership functions to optimize performance, and introducing a probabilistics framework for more accurate classification. Our findings showcase the effectiveness of these enhancements in delivering dependable sentimnent analysis results, outperforming existing approaches. While acknowledging potential influences of data quality and dataset characteristics, our study contributes to advancing text analysis techniques, particularly in sentiment analysis, with broader applications across various sectors. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	QA76.9.A43 G34 2024 (Browse shelf)	Available		FT7841

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Undergraduate Thesis : (Bachelor of Science major in Computer Science) : Pamantasan ng Lungsaod ng Maynila, 2024. 56

ABSTRACT: This study addresses fundamental challenges in sentiment analysis by enhancing the effectiveness of Fuzzy Support Vector Machines (FSVM). We propose novel methodologies to improve FSVM's interpretability, generality, and accuracy in sentiment classification. By integrating Bayesian rules, employing feature selection techniques, and optimizing fuzzy membership functions within a fuzzy logic frameqork, our approach aims to overcome the limitations of traditional FSVM. Through thorough testing and evaluation, our enhanced FSVM algorithm demonstrates superior adherence to predefined parameters, prioritizing precision and reliability in sentiment analysis tasks compares to existing methods. Specifically, we focus on refining feature selection to enhance model adaptability, incorporating fuzzy membership functions to optimize performance, and introducing a probabilistics framework for more accurate classification. Our findings showcase the effectiveness of these enhancements in delivering dependable sentimnent analysis results, outperforming existing approaches. While acknowledging potential influences of data quality and dataset characteristics, our study contributes to advancing text analysis techniques, particularly in sentiment analysis, with broader applications across various sectors.

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