Study on Key to Successful of Applying Text Mining Technology in E-Commerce on Facebook Fan Page Posts

• Main Authors: Chuang, Cheng-Tung, 莊正棟
• Other Authors: Lu, Ruei-Shan
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/19508313751301447031

碩士 === 德明財經科技大學 === 資訊管理系 === 104 === The Internet promotes the development of the social websites, and it offers the diversified information sources for the consumers. Get rid of the past restrict the dissemination of information, gradually changing the way consumer decisions. More and more consumers to search and compare products before shopping, refer to the relevant community forums product reputation. It’s the different passive receiving official product messages than before. Post content community site has gradually become the main reference for the consumer. Taiwan's consumer vulnerable to the impact bandwagon effect, with others made similar decisions, so that members post information on social networking sites have become an important "Pointer of Shopping". This study is based on text mining, use Facebook API to get 14 E-Commerce Facebook fan page posts content. Use Academia Sinica CKIP team developed the Chinese word segmentation system and sorting out literature brand personality dictionary file for analysis. To find identify the key factors affecting the fans page posts, and fans group of the brand personality. This study hopes to provide an efficient non-labor questionnaire one kind of statistical analysis methods in computer science. To provide business owners and business Facebook fan group engaged and online marketing future reference.