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KNOWLEDGE DISCOVERY TERHADAP ULASAN APLIKASI MENGGUNAKAN PERBANDINGAN WORD EMBEDDINGS DENGAN MODEL CNN-LSTM PADA SENTIMEN ANALISIS

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The rapid growth of e-commerce in Indonesia reflects the country's economic acceleration and provides a variety of benefits. One of them is TikTok Shop, which has become a popular platform for digital economic activities that offers opportunities for businesses and consumers. To maximize these opportunities, companies must effectively understand and manage user sentiment to improve services and user experience. Accurate sentiment analysis plays a crucial role in achieving this goal. This research focuses on comparing the effectiveness of various word embedding methods in enhancing sentiment analysis of TikTok Shop user reviews. The study applies a combined CNN-LSTM approach, comparing classic pre-trained embeddings like GloVe with custom-trained embeddings using Word2Vec and FastText, which are trained on domain-specific data. The objective of this research is to identify the most effective embedding method for capturing user sentiment and providing more accurate insights. The findings show that the custom-trained technique, Word2Vec, delivered the best performance, achieving the highest accuracy, precision, F1-score, and AUC-ROC values. FastText ranked second with nearly the same performance, differing only slightly. In contrast, GloVe pre-trained ability to capture user sentiment is not as strong as that of custom-trained Word2Vec and FastText, although it remains quite effective overall.

Availability
Inventory Code Barcode Call Number Location Status
2407006876T161449T1614492024Central Library (REFERENCE)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1614492024
Publisher
Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiv, 58 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.312 07
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Sistem Informasi
Analisis sentimen — Ulasan aplikasi
Specific Detail Info
-
Statement of Responsibility
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No other version available

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