Grant: Discovery Grants Program - Individual

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.