Yeongtaek Oh  Mintae Kim  Wooju Kim

10.5626/JOK.2019.46.1.45

Word2vec Long Short Term Memory sentiment analysis korean natural language processing

The sentiment analysis is a field of document classification that classifies the sensitivity of text documents. The sentiment analysis methodology that employs the use of deep learning can be divided into a process of tokenizing a document, obtaining a sentence vector through embedding and classifying a vectorized document. We reviewed the methods of various existing studies and found out the appropriate methodology focusing on embedding methods and deep learning models for the Korean documents through comparative experiments. The document pre-processing method compares documents to words, syllables and phonemes. Additionally, a comparative experiment was conducted on the Naver movie review data set nsmc (naver sentiment movie corpus) from the CNN to the LSTM, bi-LSTM, stacked bi-LSTM and the newly proposed Parallel Stacked Bidirectional LSTM model. The results showed that the performance of the proposed model was higher than that of the existing simple deep learning model. Moreover, itachieved the best classification performance of approximately 88.95% through the ensemble among the models learned through other pre-processing.