COMPARISON OF FEEDFORWARD NEURAL NETWORK AND LONG SHORT TERM MEMORY IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS

Dwi Ayu Lusia(1*), Yessica Maretha Simanjuntak(2)


(1) Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
(2) Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
(*) Corresponding Author

Abstract


Sentiment analysis is a method for generating types of views or opinions that express positive, neutral or negative sentiments. The application of sentiment analysis was carried out to determine the sentiment of Shopee application users. This research uses an artificial neural network algorithm to learn patterns from training data to predict the sentiment of the test data class. The aim of the research is to determine sentiment classification, identify the optimal Feedforward Neural Network and Long Short Term Memory architectural models in classifying user reviews of the Shopee application and compare the performance of the models based on the level of accuracy. The data set is divided into training data and test data respectively by 80% and 20%. The research results showed that there were 91.865 reviews with positive sentiment, 63.038 negative reviews and 26.662 neutral reviews based on Valanced Aware Dictionary Sentiment lexicon dictionary. The network architecture used is one hidden layer, with 137 hidden neurons and a two hidden layer model, with 491 units of first hidden neurons and 38 units of second hidden layer neurons. Evaluation of sentiment classification of Shopee application users resulted in the highest accuracy rate on the single-layer LSTM model, at 68,93%, with precision of 61,29%, and recall of 56,10%.

Keywords


Feedforward Neural Network; Long Short Term Memory; Sentiment Analysis; Shopee

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DOI: https://doi.org/10.26714/jsunimus.13.1.2025.74-84

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