Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network
Prima Dewi Purnamasari, Muhammad Taqiyuddin, Anak Agung Putri Ratna · Proceedings of the 3rd International Conference on Communication and Information Processing · 2017
One biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, computer can learn to understand human language by such learning mechanism There are many types of ANN and for this research Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used and compared on their performance. The text data for the sentiment analysis was taken from Stanford publication and transformation from text to vectors were conducted using word2vec. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35% ± 0.07 for RNN and 87.11% ± 0.50 for CNN, the training time for RNN only need 8.256 seconds while CNN need 544.366 seconds.