SBIR Phase I: A Controversy Detection Signal for Finance

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是使用争议检测来支持金融机构降低风险和增加利润的能力。这是依赖人工智能和机器学习的“替代数据”产品日益增长趋势的一部分。除了金融行业之外，争议检测技术在各种垂直市场中也有许多潜在的应用，例如危机管理、国防应用和广告技术。除了商业应用之外，通过向个人用户开放争议的分析和解释力，还可以产生社会影响。争议对公民社会产生了巨大影响，所谓的“过滤泡沫”加剧了政治和其他方面的两极分化；最近，政治光谱两边的假新闻都引起了公众的关注，并引发了政治上的担忧。这项技术商业化对社会的积极影响包括帮助用户更好地了解情况，更有能力批判性地评估往往势不可当的在线内容流。在金融等高度可量化的领域证明这种创新的可行性，可以满足该领域的客户需求，为经济创造新的就业机会，同时实现可以改善公民社会的社会公益应用。这个小企业创新研究（SBIR）第一阶段项目依靠复杂的机器学习和信息检索技术来自动检测有争议的话题。最初的数据是在马萨诸塞大学阿姆赫斯特分校收集的，使用了美国国家科学基金会支持的研究，该研究通过将网页的文本映射到算法识别的有争议的主题来识别争议。研究表明，使用现有的情感分析方法无法检测争议，情感分析是一种广泛采用的自然语言处理方法。该项目弥合了这一新兴技术的当前能力与金融领域明确的用户需求之间的差距。它将通过将实时争议检测信号应用于金融数据来评估争议检测的可行性，以降低风险并增加回报。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to use controversy detection to support financial institutions' ability to reduce their risks and increase profits. This is part of a growing trend towards "alternative data" products relying on artificial intelligence and machine learning. Besides the financial industry, there are numerous potential applications of controversy detection technology in a variety of market verticals, such as crisis management, defense applications, and advertising technology. Beyond commercial applications, there is scope for social impact by opening analysis and explanatory power of controversies to individual users. Controversies have a massive impact on civic society and the so-called "filter bubble" exacerbates polarization, both political and otherwise; fake news on both sides of the political spectrum has recently captured public attention and generated political concern. Positive impact on society from commercializing this technology includes helping users become better informed and more capable of critically evaluating the often-overwhelming stream of online content. Proving the feasibility of this innovation in a highly quantifiable space such as finance could answer a customer need in that space and create new jobs for the economy, while enabling social good applications that can improve civic society.This Small Business Innovation Research (SBIR) Phase I project relies on sophisticated machine learning and information retrieval techniques to automatically detect controversial topics. The initial data were collected at the University of Massachusetts Amherst, using NSF-supported research, which recognized controversy by mapping the text of a webpage to algorithmically-identified controversial topics. Research has demonstrated that controversy cannot be detected using existing methods of sentiment analysis, a widely-adopted natural language processing method. This project bridges the gap between the current capabilities of this nascent technology and the clear user need in the financial domain. It will evaluate the feasibility of controversy detection by applying a real-time controversy detection signal to financial data to reduce risk and increase returns.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.