| Autor: |
Gina Rosalinda, Rukun Santoso, Puspita Kartikasari |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Jurnal Gaussian. 11:554-561 |
| ISSN: |
2339-2541 |
| DOI: |
10.14710/j.gauss.11.4.554-561 |
| Popis: |
Reviews given by application users have an essential role in the decisions of potential users before deciding to use a service. Users can provide reviews of application services on the Google Play Store. A large number of reviews makes the data collection and analysis difficult if done manually. Therefore, a particular method is needed to collect and analyze these reviews automatically to obtain the hidden essential information. Topic modeling is an extension of text analysis that can find main themes or trends hidden in large sets of unstructured documents. This study applies topic modeling with the Latent Dirichlet Allocation (LDA) method for Netflix application review data sourced from the Google Play Store web. The Latent Dirichlet Allocation (LDA) method is a probability model from textual data that can explain the hidden semantic themes in the review document. This research aims to analyze hidden topics that application users discuss. The results of topic modeling show that of the twelve topics generated, the most discussed topic by users is payment methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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