Comparison of word embedding features using deep learning in sentiment analysis

Abstract

In this research, we use several deep learning methods with the word embedding feature to see their effect on increasing the evaluation value of classification performance from processing sentiment analysis data. The deep learning methods used are conditional random field (CRF), bidirectional long short term memory (BLSTM) and convolutional neural network (CNN). Our test uses social media data from Netflix application user comments. Through experimentation on different iterations of various deep learning techniques alongside multiple word embedding characteristics, the BLSTM algorithm achieved the most notable accuracy rate of 79.5% prior to integrating word embedding features. On the other hand, the highest accuracy value results when using the word embedding feature can be seen in the BLSTM algorithm which uses the word to vector (Word2Vec) feature with a value of 87.1%. Meanwhile, a very significant change in value increase was obtained from the FastText feature in the CNN algorithm. After all the evaluation processes were carried out, the best classification evaluation results were obtained, namely the BLSTM algorithm with stable values on all word embedding features.