精品水蜜桃久久久久久久,成人国产精品动漫欧美一区,亚洲爆乳精品无码一区二区,精品人妻系列无码人妻免费视频,6080yyy午夜理论AA片,动漫精品无码一区二区三区,日韩欧美国产传媒第一区二区,国产91高潮操逼视频流白浆,97国内少妇偷人精品视频免费 ,亚洲国产成人精品久久久国产成人一区二区三区综合区精品久久久中文字幕一区,亚洲精品久久久一区黄无码国产a一级无码毛片一区二区三区,久久久无码国产精精品免费国国产欧美日本韩高清视频一区二区三区免费式,国产成人无码精品久久久免费,精品欧美国产一区二区三区不卡 ,国内精品久久久久久久影视麻豆|国产精品无码亚洲|无限国产资源好片2018|精品91自产拍在线观看|精品乱子伦一区二区三区掼蛋

主講人：樊衛(wèi)國 美國弗吉尼亞理工大學(xué)教授

時(shí)  間：2013年6月25日（星期二）下午4:00-5:30

地  點(diǎn)：bwin必贏唯一官網(wǎng)313教室

Abstract：The recent surge in the usage of social media, such as Facebook, Twitter, and online communities and forums has created an enormous amount of user-generated content (UGC). UGC serves as a huge gold mine that is yet to be tapped for various business and consumer intelligence applications.  While there are streams of research that seek to mine this UGC, these research studies, however, are often done in a piecemeal fashion without a coherent and systematic framework.  In this paper, we synthesize existing research studies and propose an integrated text analytic framework based on statistical language processing and computational linguistics.  The framework effectively leverages the rich social media contents and quantifies the words in the text using various automatically extracted signal cues.  These extracted signal cues can then be used as modeling inputs for business value discovery.  We show case the usefulness of the framework by performing product defect discovery using UGC from popular online discussion forums. Implications of such a framework for both research and practice are provided.