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• Main Authors: Hsiu-Ying Shih, 石秀媖
• Other Authors: Shi-Jen Lin
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/ekkw54

碩士 === 國立中央大學 === 資訊管理學系 === 106 === With the popularization of Internet and technology, people get more information through the Internet, but the amount of information has increased dramatically. Excessive information has gradually formed the problem of information explosion. Digital documents of various enterprises and organizations are also constantly increasing. The amount of digital documents is large to be difficult to manage and utilize effectively. Text Classification is created in response to deal with the massive surge in classification needs. Traditional text classification is done manually. In recent years, Deep Learning has been widely discussed and applied in a variety of studies. Many literatures show that deep learning techniques can help improve results or improve performance. This study uses the data of product names purchased by consumers in physical store to apply the word2vec word embedding model to the automatic classification of documents through the deep learning technology. By self-learning the semantic relationship, the products are automatically classified into the correct category. And through a number of experiments to explore the word2vec word embedding model trained under different factors, will affect its effectiveness. Finally, this study confirmed that applied word2vec to expand keywords can improve the effect of classification.