SBIR Phase II: Automated Community and Sentiment Mining for Global Media Preference Understanding

SBIR第二阶段项目将数据挖掘和机器学习技术应用于自然语言描述和互联网链接图，以建立社区模型，以预测不同类型媒体(音乐、电视、在线媒体、视频游戏、书籍)的偏好、品味和情绪。当前的上下文信息挖掘方法扫描页面上的文本以寻找广告或推荐，而忽略了大多数自我发布的互联网讨论中固有的有价值的社区联系。在全文上运行的情感和观点提取系统产生了具有挑战性的语言分析问题，充满了规模和适应性的问题。身份识别系统可以根据匿名网络作者或网站访问者对多种媒体的喜好，自动将他们归类到特定的人口统计社区。第二阶段的研究项目使用基于已知在线语料库的训练的无偏见学习模型来实现意见提取，这些语料库可以适应不同的语言，并随着更多的数据变得更高的准确性而实时学习。当前的个性化和营销方法要么着眼于匿名用户的“点击流”，导致对流行电影和音乐的同样匿名的推荐--或者通过扫描文本的表层概述，导致对娱乐内容和社区情绪的上下文理解有限的关键词广告。该项目计划将以人为中心的社区和情绪分析技术完全整合到为数字娱乐提供商和营销人员提供的自主、学习和无规模的“媒体知识服务”中，该服务可以改变数字内容的营销和销售方式。

This SBIR Phase II project applies data mining and machine learning techniques to both natural language description and Internet link graphs to model communities in order to predict preference, taste and sentiment for different kinds of media (music, TV, online media, video games, books). Current contextual information mining approaches that scan the text on a page for advertisement or recommendation ignore valuable community connections inherent in most self-published Internet discussion. Sentiment and opinion extraction systems operating on full text create challenging language parsing problems are fraught with issues of scale and adaptability. The identification systems can automatically categorize anonymous Internet writers or website visitors into specific demographic communities based on their tastes in many kinds of media. The Phase II research project approaches opinion extraction with a bias-free learning model based on training from known online corpuses that can be adapted to different languages and learns in real time as more data becomes available for high accuracy.Current personalization and marketing approaches either look at the "clickstream" of an anonymous user, leading to equally anonymous recommendations for popular movies and music -- or by scanning a surface-level overview of the text, leading to keyword advertisements with limited contextual understanding of entertainment content and community sentiment. The project plans to fully integrate people-focused community and sentiment analysis technologies into an autonomous, learning and scale-free "media knowledge service" for digital entertainment providers and marketers that can change the way digital content is marketed and sold.