Using opinion mining to create user profile of seller in online auction

• Main Authors: Shyan Leu, 呂璇
• Other Authors: Shi-Jen Lin
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/55942957058791856462

碩士 === 國立中央大學 === 資訊管理研究所 === 99 === Online auctions have become immensely popular and created massive cash turnover in recent years. However, for a user intent on purchasing an item from an auction site, selecting an appropriate seller from the numerous choices is not an easy task. The emergence of Internet has constructed a space for users to freely express opinions and exchange experiences regarding products, services, and any public issues. Buyers in online auctions write feedback opinions to the sellers from whom they have bought the items. Other buyer read these opinions to help them determine which seller and item to bid for. In general, many large auction sites in Taiwan (for example: Yahoo auction, Ruten auction) showed only the most basic binary evaluation system, let user rate the seller in three levels, ‘‘good is +1’’, ‘‘bad is -1’’, or ‘‘neither is 0,’’ and calculate the total evaluation of the seller. However, the total evaluation can’t be the judge of merits to two different sellers, and let buyers understand the seller of the product quality, service, and delivery speed. In this research, we aim at helping buyers to make decision in online auction by creating a seller’s user profile. First, we use opinion mining technology to filter out comments, automatically tagged and classified meaningful features under the thesaurus. Second, calculating the four dimensions (product quality, service, delivery speed, and overall evaluation) scores by emotion scales extended from sentimental analysis in the past. Finally, we also take into account the credibility of the buyers that decreases the weight because of the similarity of their opinions. The credibility calculated by algorithms integrated with related research, improving the existing evaluation mechanisms.