PERBANDINGAN TF-IDF DAN WORD2VEC UNTUK ANALISIS SENTIMEN MENGGUNAKAN SUPPORT VECTOR MACHINE

ANGREINI, CINDY and Utami, Alvi Syahrini (2024) PERBANDINGAN TF-IDF DAN WORD2VEC UNTUK ANALISIS SENTIMEN MENGGUNAKAN SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis is a branch of Natural Language Processing (NLP) used to determine public opinions on specific topics as positive, negative, or neutral. This study aims to compare the performance of two feature extraction methods across three scenarios: TF-IDF, Word2Vec-CBOW, and Word2Vec-skipgram. The dataset utilized consists of comments from the Instagram platform @magangmerdeka regarding MSIB 7. The model was developed using the Support Vector Machine (SVM) algorithm with a linear kernel, and the data was split into training, validation, and test sets in an 80:10:10 ratio. Evaluation was conducted using a confusion matrix and evaluation metrics. The results show that the TF-IDF feature extraction method achieved the highest accuracy of 80.63%, compared to the Word2Vec methods, CBOW and skipgram, which achieved 69.38% and 71.88%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	TF-IDF, Word2Vec, CBOW, Skipgram, SVM, Sentiment Analysis
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T10.5-11.9 Communication of technical information
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Cindy Angreini
Date Deposited:	07 Jan 2025 02:37
Last Modified:	07 Jan 2025 02:38
URI:	http://repository.unsri.ac.id/id/eprint/162781

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