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040			_erda
041			_aengtag
050			_aQA267 A73 2024
082			_a.
100			_aEunna Jazrel M. Arcilla, Patricia Mae A. Samson.
245		0	_aEnhancement of support vector machine utilizing Roberta applied to sentiment analysis of facebook data.
264			_a. _b. _c2024
300			_c. _bUndergraduate Thesis: (Bachelor pf Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024.
336			_b. _atext _2rdacontent
337			_30 _b. _aunmediated _2rdamedia
338			_30 _b. _avolume _2rdacarrier
505			_aABSTRACT: Many people are using social media sites lie Facebook to express their opinions, experiences. Or whatever they want to post online. Understanding user sentiment has become crucial for various applications, ranging from marketing to public opinion analysis. Researchers use natural language processing (NLP) and machine learning algorithms to evaluate textual information from Facebook posts and classify sentiments as positive, negative, or neutral. This study delves into sentiment analysis of Facebook data to better understand how users express their emotions. Additionally, the method addresses the limitations of sentiment analysis on social media due to informal language, slang, and context-dependent phrases. The study aims to develop an enhanced Support Vector Machine algorithm for sentiment analysis of Facebook data by utilizing the RoBERTa ( A Robustly optimized BERT) model. To enhanced sentiment accuracy, thus the performance of the traditional SVM algorithm, the proposed approach uses VADER to predict initial sentiment labels, loads a pre-trained RoBERTa model as preprocessing techniques, fine-tunes the RoBERTa model and extracts RoBERTa embeddings to optimize the SVM algorithm. This improves the model’s capacity to handle imbalanced datasets and efficiently manage larger datasets while filtering out noisy or irrelevant characteristics. To analyse the performance of the proposed technique, results are compared with the result of existing algorithms. The enhanced SVM algorithm significantly outperforms the existing approach in terms of accuracy, precision, recall, and F1-score, with a 4% to 8% improvement in accuracy over the previous algorithm. This research highlights the potential of integrating RoBERTa techniques with SVM for enhanced sentiment analysis.
506			_a5
520			_aABSTRACT: Many people are using social media sites lie Facebook to express their opinions, experiences. Or whatever they want to post online. Understanding user sentiment has become crucial for various applications, ranging from marketing to public opinion analysis. Researchers use natural language processing (NLP) and machine learning algorithms to evaluate textual information from Facebook posts and classify sentiments as positive, negative, or neutral. This study delves into sentiment analysis of Facebook data to better understand how users express their emotions. Additionally, the method addresses the limitations of sentiment analysis on social media due to informal language, slang, and context-dependent phrases. The study aims to develop an enhanced Support Vector Machine algorithm for sentiment analysis of Facebook data by utilizing the RoBERTa ( A Robustly optimized BERT) model. To enhanced sentiment accuracy, thus the performance of the traditional SVM algorithm, the proposed approach uses VADER to predict initial sentiment labels, loads a pre-trained RoBERTa model as preprocessing techniques, fine-tunes the RoBERTa model and extracts RoBERTa embeddings to optimize the SVM algorithm. This improves the model's capacity to handle imbalanced datasets and efficiently manage larger datasets while filtering out noisy or irrelevant characteristics. To analyse the performance of the proposed technique, results are compared with the result of existing algorithms. The enhanced SVM algorithm significantly outperforms the existing approach in terms of accuracy, precision, recall, and F1-score, with a 4% to 8% improvement in accuracy over the previous algorithm. This research highlights the potential of integrating RoBERTa techniques with SVM for enhanced sentiment analysis.
526			_aF
540			_a5
655			_aacademic writing
942			_alcc _cMS _01 _2lcc
999			_c25266 _d25266