000			02629nam a22002417a 4500
003			ft8879
005			20251215162023.0
008			251215b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9.A43 G34 2024
082			_a.
100	1		_a Alegria, Froilan M.; Bautista, Kurt John Adrianne C.; De Leon, Alfred Alwyn I.
245			_aAn enhancement of fuzzy support vector machine using natural language processing model applied on online product reviews
264		1	_a. _b. _cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: 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			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37348 _d37348