Current research in finance and the social sciences utilizes sentiment analysis to understand human decisions in response to textual materials. While sentiment analysis has received great traction lately, the available tools are not yet living up to the needs of researchers. Especially R has not yet capabilities that most research desires.
Our package “SentimentAnalysis” performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as General Inquirer, Harvard IV or Loughran-McDonald. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogeneous response variable.
This immediately reveals manifold implications for practitioners, as well as those involved in the fields of finance research and the social sciences: researchers can use R to extract text components that are relevant for readers and test their hypotheses on this basis. By the same token, practitioners can measure which wording actually matters to their readership and enhance their writing accordingly. We demonstrate the added benefits in two case studies drawn from finance and the social sciences.