Using the dictionary-based approach to measure the sentiment of finance-related texts is primarily focused on English-speaking content. This is due to the need for domain-specific dictionaries and the primary availability of those in English. Through the contribution of Bannier et al. (2019b),...

Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining and exploratory...

We construct new technology indicators using textual analysis of patent documents and occupation task descriptions that span almost two centuries (1850-2010). At the industry level, improvements in technology are associated with higher labor productivity but a decline in the labor share....

Much of the research in law and finance reduces long, complex texts down to a small number of variables. Examples include the coding of corporate charters as an entrenchment index or characterizing dense securities complaints by using variables that capture the amount at issue, the statutes...

In this paper, we introduce a framework that simplifies the process of finding information about a city. This is helpful when people are looking for information on various cities before planning a trip. As visiting various online portals and collecting information is a time consuming and tedious...

Prior research examining the impact of corporate social responsibility (CSR) on stakeholders has largely focused on studying the insurance like effect of CSR outlays and the resulting effect on stock returns, thereby emphasizing the implications for shareholders. Our study examines the following...