Applying Deep Learning for Sentiment Analysis on Word of Mouth of Smart Bracelet

• Main Authors: Hung-Chou Teng, 鄧宏洲
• Other Authors: Min-Yuh Day
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/2d94uu

碩士 === 淡江大學 === 資訊管理學系碩士在職專班 === 105 === The rise of social networking, many consumers are willing to discuss in the community media to share, express their views on the product. Enterprises can analyze the consumers'' preferences and advantages and disadvantages of the various products on the market through a large number of online reviews, but in the past the literature is less applied to the Deep Learning in the Sentiment Analysis of Chinese comments. The contribution of this thesis is to construct a sentiment dictionary which belongs to the field of Smart Bracelet. And applying Deep Learning and Recursive Neural Network Long Short Memory technology in the Smart Bracelet word of mouth Sentiment Analysis. And compared with the results of Naïve Bayes algorithm and Support Vector Machine. The experimental results show that the correct rate of Naïve Bayes algorithm is 70.67%, the Support Vector Machine is 66.01%, and Deep Learning is 89.94%. So as to prove Deep Learning in the Sentiment Analysis of the most effective prediction.