"Sentometrics": Econometrics of textual sentiment with applications in economics and finance

“Sentometrics”：文本情感计量经济学及其在经济和金融中的应用

• 批准号: RGPIN-2022-03767
• 负责人: Ardia, David
• 金额: $ 1.97万
• 依托单位: HEC Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765248
• 关键词: Sentometrics; Econometrics; textual; sentiment; applications

There is a long-standing tradition of using sentiment as either a parameter or a variable in econometric modeling. Historically, the use of questionnaires and proxies to quantify sentiment variables has been predominant. In recent years, it has become popular to analyze the sentiment embedded in textual data due to the digitalization of communication media and progress in natural language processing and machine learning techniques. Determining whether media are carriers of potentially valuable information for economic and financial analysis is among the objectives of the "Sentometrics" research agenda. Sentometrics bridges econometrics and machine learning techniques to investigate the transformation of large volumes of qualitative textual sentiment data into quantitative sentiment variables and their subsequent application in analyzing the relationship between sentiment and other variables. The long-term objective of this project is to design new econometric tools to exploit more information on the dynamics and sources of sentiment in news media articles. The outputs of this research project will fill several research gaps in that direction. First, it will develop models to determine whether news media help anticipate changes in market regimes of economic and financial variables. Such models and analyses are currently lacking. Second, it will investigate if lexicons can be designed with the specific goal of improving financial risk forecasts. While lexicons have the advantage of not being black boxes, the current practice is to rely on human-annotated dictionaries, which are rather generic and may contain biases. Third, the proposal will construct algorithms to improve the estimation of joint sentiment-topic models. These models are not yet widely used due to their computational costs and poor convergence. We expect to design algorithms to render them practical and widen the scope of their usage. Finally, the open-source code developed at each phase of the project will foster the deployment of the methodologies in the community to extend the tools or develop new applications. Improved topic extraction and domain-specific lexicon construction are relevant in other fields such as marketing and policy monitoring. In this context, the openness of the proposed project is very relevant and will therefore enhance direct knowledge transfer.

在计量经济学建模中使用情绪作为参数或变量有着悠久的传统。从历史上看，主要使用问卷和代理来量化情绪变量。近年来，由于通信媒体的数字化以及自然语言处理和机器学习技术的进步，分析文本数据中嵌入的情感变得流行。确定媒体是否是经济和金融分析潜在有价值信息的载体是“Sentometrics”研究议程的目标之一。 Sentometrics 将计量经济学和机器学习技术联系起来，研究将大量定性文本情感数据转换为定量情感变量，以及它们在分析情感与其他变量之间的关系中的后续应用。 该项目的长期目标是设计新的计量经济学工具，以利用有关新闻媒体文章中的动态和情绪来源的更多信息。该研究项目的成果将填补该方向的多个研究空白。首先，它将开发模型来确定新闻媒体是否有助于预测经济和金融变量的市场机制的变化。目前缺乏这样的模型和分析。其次，它将调查词典的设计是否可以以改善金融风险预测为具体目标。虽然词典具有不是黑匣子的优点，但目前的做法是依赖人工注释的词典，这些词典相当通用并且可能包含偏见。第三，该提案将构建算法来改进联合情感主题模型的估计。由于计算成本高且收敛性差，这些模型尚未广泛使用。我们期望设计算法使其实用并扩大其使用范围。最后，项目每个阶段开发的开源代码将促进社区中方法的部署，以扩展工具或开发新应用程序。改进的主题提取和特定领域的词典构建与营销和政策监控等其他领域相关。在这种情况下，拟议项目的开放性非常重要，因此将加强直接知识转移。