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

该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，以模拟社区，从而预测不同类型媒体（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，扫描广告或推荐页面上的文本，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。在全文上操作的情感和意见提取系统产生具有挑战性的语言解析问题，这些问题充满了规模和适应性问题。 识别系统可以自动分类匿名的互联网作家或网站访问者到特定的人口统计社区的基础上，他们的口味在许多种媒体。第二阶段的研究项目采用一种无偏见的学习模型进行意见提取，该模型基于已知在线语料库的训练，可以适应不同的语言，并随着更多数据的可用性而进行真实的实时学习，以实现高准确性。当前的个性化和营销方法要么关注匿名用户的“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面水平概述，导致对娱乐内容和社区情感的有限上下文理解的关键字广告。 该项目计划将以人为本的社区和情感分析技术完全整合到一个自主的、学习的和无规模的“媒体知识服务”中，为数字娱乐提供商和营销人员提供服务，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.