Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments

Maswan Pratama Putra, Y. Sibaroni
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引用次数: 0

Abstract

In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value
纪元值对 RNN-LSTM 算法在 Lazada 应用程序评论情感分类中的性能的影响
在当今的发展中,卖方和买方之间的买卖交易过程非常发达,不仅可以直接进行,还可以在网上进行,或称为电子商务。技术发展如此之快,间接鼓励了企业家通过电子商务发展。Lazada 是印尼拥有众多用户的网上商店之一,Lazada 让购物变得简单,无需亲临现场或直接购物。但是,使用电子商务购买商品会遇到商品质量问题,因此,可以通过对每件要购买的商品的评论来了解商品的质量。情感分析是使用带有长短期记忆(LSTM)的循环神经网络(RNN)方法进行的。在处理验证数据和测试数据时,使用 Epoch 值作为参数,以产生最佳准确度值。
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