Performance Analysis of Hybrid Architectures of Deep Learning for Indonesian Sentiment Analysis

Abstract

Sentiment analysis is one of the fields of Natural Language Processing that builds a system to recognize and extract opinions in the form of text into positive or negative sentiment. Nowadays, many researchers have developed methods that yield the best accuracy in performing analysis sentiment. Three particular models are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), which have deep learning architectures. CNN is used because of its ability to extract essential features from each sentence fragment, while LSTM and GRU are used because of their ability to memorize prior inputs. GRU has a more straightforward and more practical structure compared to LSTM. These models have been combined into hybrid architectures of LSTM-CNN, CNN-LSTM, and CNN-GRU. In this paper, we analyze the performance of the hybrid architectures for Indonesian sentiment analysis in e-commerce reviews. Besides all three combined models mentioned above, we consider one more combined model, which is GRU-CNN. We evaluate the performance of each model, then compare the accuracy of the standard models with the combined models to see if the combined models can improve the performance of the standard. Our simulations show that almost all of the hybrid architectures give better accuracies than the standard models. Moreover, the hybrid architecture of LSTM-CNN reaches slightly better accuracies than other hybrid architectures.