A Study of Sentiment Analysis of Short Chinese Comments with Attribute Classification

• Main Authors: Ho Chang, 張鶴
• Other Authors: Guan-Ling Lee
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/2m58yk

碩士 === 國立東華大學 === 資訊工程學系 === 106 === Nowadays, technology is changing with each passing day. The communication way among people has changed from writing letters to using social network, and it is no longer limited by distance. As people get used to using Internet, from product reviews to paper journals, there are various articles which are available to be searched on the Internet. Internet has already got into people’s lives. However, it is not easy to figure out useful knowledge in such a large amount of data. Thus, recently, big data, data mining and machine learning have been valued gradually. In the light of this, the purpose of this paper is to establish a multi-attribute short article sentiment analysis mode. In the beginning, we use NTUSD and HOWNET dictionary to build a vocabulary database, and then collect a large amount of data as training data, hand-mark the attributes, and then pass the data to CKIP to disassemble the short comment. The machine will analyze the polarity of each attribute keyword and emotional letters, establish the keyword database and expand the polarity of emotion letters in positive and negative vocabulary database. To verify the correctness of the proposed method, we compare the difference between the scores obtained by scoring manually and the scores calculated by applying the method in this paper. Then remove the naive Bayesian classifier of the system and use the original negative lexicon data for sentiment analysis, and compare the result obtained by scoring manually with the result obtained by applying the method of this paper. The experimental results show that the method proposed in this paper has certain accuracy.