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

在计量经济建模中，使用情绪作为参数或变量的传统由来已久。从历史上看，使用问卷和代理来量化情绪变量一直占主导地位。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已成为流行。确定媒体是否是经济和金融分析的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结合起来，研究将大量定性文本情感数据转换为定量情感变量，并将其随后应用于分析情感与其他变量之间的关系。该项目的长期目标是设计新的计量经济学工具，以利用新闻媒体文章中情绪的动态和来源的更多信息。这一研究项目的成果将填补这方面的若干研究空白。首先，它将开发模型，以确定新闻媒体是否有助于预测经济和金融变量的市场制度的变化。目前还缺乏这样的模型和分析。其次，研究是否可以设计以提高财务风险预测为特定目标的词汇。虽然词典的优势在于不是黑盒，但目前的做法是依赖于人工注释的词典，这些词典相当通用，可能包含偏见。第三，本文将构建改进联合情感主题模型估计的算法。由于计算成本和收敛性差，这些模型尚未得到广泛应用。我们期望设计算法使其实用，并扩大其使用范围。最后，在项目的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的应用程序。改进的主题提取和特定于领域的词典构建与其他领域（如市场营销和策略监控）相关。在这种情况下，拟议项目的开放性是非常相关的，因此将加强直接的知识转移。

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.