ANALISIS SENTIMEN ULASAN PRODUK MENGGUNAKAN BIDIRECTIONAL LONG SHORT-TERM MEMORY DAN WORD EMBEDDING

HAIRUNNISA, NADIA RIZKY and Abdiansah, Abdiansah and Yusliani, Novi (2023) ANALISIS SENTIMEN ULASAN PRODUK MENGGUNAKAN BIDIRECTIONAL LONG SHORT-TERM MEMORY DAN WORD EMBEDDING. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis is a computational study of human opinions, sentiments, emotions, and behavior toward entities or attributes expressed through written text. Sentiment analysis plays a significant role for companies and organizations because public opinion about their products and services is valuable for business strategy and evaluation. This research developed a system to classify product review sentiment using Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm and Word Embedding, Word2Vec as the embedding layer. The built system uses two models, the base model, which has the same parameter configuration as the CNN model used in previous research, and the tuned model, whose parameter configuration is based on the results of hyperparameter tuning. The results showed that the second model has the best performance with an accuracy of 90.33%, precision of 99.41%, recall of 90.29%, and F1-Score of 94.61%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentimen Analisis, Ulasan Produk, Bidirectional Long Short Term-Memory, Word Embedding, Word2Vec
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nadia Rizky Hairunnisa
Date Deposited:	29 May 2023 04:15
Last Modified:	29 May 2023 04:15
URI:	http://repository.unsri.ac.id/id/eprint/105630

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