The Application of Deep Learning in Chinese Public Opinion Analysis– A Case Study of BERT

• Main Authors: Shih-Mu Jhong, 鍾士慕
• Other Authors: Chao-Chang Chiu
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/2f95me

碩士 === 元智大學 === 資訊管理學系 === 107 === Language is the carrier of thought, the most natural tool for human beings to exchange ideas and express emotions, and the unique nature of human beings from other species. It is an important research direction in the field of artificial intelligence, and text classification has always been in natural language processing. A wide range of projects, from the early TF-IDF and machine learning to the Word2Vec method published by Google in 2013, and finally to the new trend of the Transformer model, the various algorithms for text classification have been continuously improving, especially in depth. After learning, the trend is more obvious. This study uses algorithms commonly used in text categorization to analyze and compare, from traditional TF-IDF with SVM, LG, RF, CART and other machine learning methods to the current common Word2vec and LSTM and the latest trend Transformer model BERT, mainly focusing on the ratio Teach and analyze various old and new algorithms in text classification from previous to present, and evaluate the performance of these algorithms in traditional Chinese text classification. The data is collected using Chinese lyric data collected in the traditional Chinese discussion forum. The traditional TF-IDF still shows good performance in text classification.