Method for the Automatic Generation of Training Sets for Word Embedding Reflecting Sentiment Information

Dahee Lee, Won-Min Lee, Byung-Won On

http://doi.org/10.5626/JOK.2022.49.1.42

Word embedding is a method of expressing a word as a vector. However, since existing word embedding methods predict words that appear together, they are expressed as similar vectors even if they have different emotions. When building a sentiment analysis model using this, sentences with similar patterns may be classified into the same polarity, which is one of the factors that degrade the performance of the emotional analysis model. In this paper, to address the problem, we proposed the automatic generation of a training set for word embedding reflecting sentiment information using morpheme analysis, dependence parsing, and a sentiment dictionary. Using sentiment-specific word embedding vectors generated by the proposed model, we showed that the proposed sentiment-specific word embedding model outperformed the existing word embedding models including CBOW, Skip-Gram, FastText, ELMo, and BERT.