ANALISIS SENTIMEN ULASAN PRODUK MENGGUNAKAN BIDIRECTIONAL LONG SHORT-TERM MEMORY DAN WORD EMBEDDING
Total View This Week0

Institusion

Universitas Sriwijaya

Author

HAIRUNNISA, NADIA RIZKY
Abdiansah, Abdiansah
Yusliani, Novi

Subject

QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. 

Datestamp

2023-05-29 04:15:39 

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%. 

File :
RAMA_55201_09021281823039.pdf
RAMA_55201_09021281823039_TURNITIN.pdf
RAMA_55201_09021281823039_0001108401_0008118205_01_front_ref.pdf
RAMA_55201_09021281823039_0001108401_0008118205_02.pdf
RAMA_55201_09021281823039_0001108401_0008118205_03.pdf
RAMA_55201_09021281823039_0001108401_0008118205_05.pdf
RAMA_55201_09021281823039_0001108401_0008118205_04.pdf
RAMA_55201_09021281823039_0001108401_0008118205_06.pdf
RAMA_55201_09021281823039_0001108401_0008118205_07_ref.pdf
RAMA_55201_09021281823039_0001108401_0008118205_08_lamp.pdf