Research in finance and social sciences nowadays utilizes content analysis to understand human decisions in the face of textual materials. While content analysis has received great traction lately, the available tools are not yet living up to the needs of researchers. As our contribution, we propose, implement and demonstrate a novel approach to study tone, sentiment and reception of textual materials in R. Our approach utilizes Bayesian learning to extract words from documents that statistically feature a positive and negative polarity. This immediately reveals manifold implications for practitioners, finance research and social sciences: researchers can use R to extract text components that are relevant for readers and test their hypothesis based on these. On the other hand, practitioners can measure which wording actually matters to their readership and enhance their writing accordingly. We demonstrate the added benefits in two case studies from finance and social sciences. We also incorporate our algorithm together with common baselines for sentiment analysis in a new R package. It overcomes possible shortcomings in the existing choice of packages by providing a comprehensive toolset for sentiment analysis –supporting both a broad range of dictionary-based approaches and machine learning. Our R package effortlessly performs sentiment analysis of written materials and offers built-in functionality tailored for content analysis.
