Evaluation of the Effectiveness of Neural Network Models for Analyzing Customer Review Sentiments on Marketplace
DOI:
https://doi.org/10.57152/predatecs.v2i1.1100Keywords:
Bidirectional Gated Recurrent Unit, Customer Reviews, Gated Recurrent Unit, Long-ShortTerm MemoryAbstract
According to the 2019 report, Tokopedia is the most visited marketplace with 140,000,000 visitors per month, making it one of the most popular marketplaces in Indonesia. Customers have the opportunity to write reviews about the products they purchase at the end of the transaction process on Tokopedia. The aim of this research is to conduct sentiment analysis on product reviews on Tokopedia. Three neural networks that will be used for text classification are Bi-GRU, GRU, and LSTM. The data processing technique is divided into training and testing samples, split into 80%:20% using the holdout technique. The BI-GRU algorithm has an accuracy of 0.93% and precision of 0.96, better than the other two methods LSTM and GRU, which each have an accuracy of 0.92 and recall of 0.91.
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