  A text-mining-based word class categorization method and LSTM-based
vocabulary pattern prediction method are introduced in this paper. A
preprocessing method based on simple text appearance frequency analysis is
first described. This method was developed as a data screening tool but showed
4.35 ~ 6.21 times higher than previous works. An LSTM deep learning method is
also suggested for vocabulary appearance pattern prediction method. AI performs
a regression with various size of data window of previous exams to predict the
probabilities of word appearance in the next exam. Predicted values of AI over
various data windows are processed into a single score as a weighted sum, which
we call an "AI-Score", which represents the probability of word appearance in
next year's exam. Suggested method showed 100% accuracy at the range 100-score
area and showed only 1.7% error of prediction in the section where the scores
were over 60 points. All source codes are freely available at the authors' Git
Hub repository. (https://github.com/needleworm/bigdata_voca)
